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  <div class="headertitle"><div class="title">Nodes.cu</div></div>
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<div class="fragment"><div class="line"><a id="l00001" name="l00001"></a><span class="lineno">    1</span><span class="preprocessor">#include &quot;<a class="code" href="_nodes_8cuh.html">NeuZephyr/Nodes.cuh</a>&quot;</span></div>
<div class="line"><a id="l00002" name="l00002"></a><span class="lineno">    2</span><span class="preprocessor">#include &quot;<a class="code" href="_operation_kernels_8cuh.html">NeuZephyr/OperationKernels.cuh</a>&quot;</span></div>
<div class="line"><a id="l00003" name="l00003"></a><span class="lineno">    3</span><span class="preprocessor">#include &quot;NeuZephyr/utils.cuh&quot;</span></div>
<div class="line"><a id="l00004" name="l00004"></a><span class="lineno">    4</span><span class="preprocessor">#include &quot;NeuZephyr/StreamManager.cuh&quot;</span></div>
<div class="line"><a id="l00005" name="l00005"></a><span class="lineno">    5</span><span class="preprocessor">#include &quot;NeuZephyr/TensorOperations.cuh&quot;</span></div>
<div class="line"><a id="l00006" name="l00006"></a><span class="lineno">    6</span> </div>
<div class="line"><a id="l00007" name="l00007"></a><span class="lineno">    7</span><span class="keyword">namespace </span><a class="code hl_namespace" href="namespacenz_1_1nodes.html">nz::nodes</a> {</div>
<div class="line"><a id="l00008" name="l00008"></a><span class="lineno">    8</span>    <span class="keyword">using namespace </span>krnl;</div>
<div class="line"><a id="l00009" name="l00009"></a><span class="lineno">    9</span> </div>
<div class="foldopen" id="foldopen00010" data-start="{" data-end="}">
<div class="line"><a id="l00010" name="l00010"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1_node.html#a687ee9c34eb61f8f28caa201ca42696e">   10</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1_node.html#a687ee9c34eb61f8f28caa201ca42696e">Node::print</a>(std::ostream&amp; os)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00011" name="l00011"></a><span class="lineno">   11</span>        os &lt;&lt; <span class="stringliteral">&quot;Type: &quot;</span> &lt;&lt; type &lt;&lt; std::endl;</div>
<div class="line"><a id="l00012" name="l00012"></a><span class="lineno">   12</span>        os &lt;&lt; *output &lt;&lt; std::flush;</div>
<div class="line"><a id="l00013" name="l00013"></a><span class="lineno">   13</span>    }</div>
</div>
<div class="line"><a id="l00014" name="l00014"></a><span class="lineno">   14</span> </div>
<div class="foldopen" id="foldopen00015" data-start="{" data-end="}">
<div class="line"><a id="l00015" name="l00015"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1_node.html#a9b85913e12422bb4ac2fff483427bb47">   15</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1_node.html#a9b85913e12422bb4ac2fff483427bb47">Node::dataInject</a>(Tensor::value_type* data, <span class="keyword">const</span> <span class="keywordtype">bool</span> grad)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00016" name="l00016"></a><span class="lineno">   16</span>        output-&gt;dataInject(data, grad);</div>
<div class="line"><a id="l00017" name="l00017"></a><span class="lineno">   17</span>    }</div>
</div>
<div class="line"><a id="l00018" name="l00018"></a><span class="lineno">   18</span> </div>
<div class="foldopen" id="foldopen00019" data-start="{" data-end="}">
<div class="line"><a id="l00019" name="l00019"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1_node.html#af8b4bab3271df92ca1f0914f7a97b1e8">   19</a></span>    <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1_node.html#a9b85913e12422bb4ac2fff483427bb47">Node::dataInject</a>(<span class="keyword">const</span> std::initializer_list&lt;Tensor::value_type&gt;&amp; data, <span class="keyword">const</span> <span class="keywordtype">bool</span> grad)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00020" name="l00020"></a><span class="lineno">   20</span>        output-&gt;dataInject(data, grad);</div>
<div class="line"><a id="l00021" name="l00021"></a><span class="lineno">   21</span>    }</div>
</div>
<div class="line"><a id="l00022" name="l00022"></a><span class="lineno">   22</span> </div>
<div class="line"><a id="l00023" name="l00023"></a><span class="lineno">   23</span>    <span class="keyword">namespace </span>io {</div>
<div class="foldopen" id="foldopen00024" data-start="{" data-end="}">
<div class="line"><a id="l00024" name="l00024"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1io_1_1_input_node.html#a217dbf39ca3882f5e514357f72f29458">   24</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1io_1_1_input_node.html#a217dbf39ca3882f5e514357f72f29458">InputNode::InputNode</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">Tensor::shape_type</a>&amp; shape, <span class="keywordtype">bool</span> requires_grad) {</div>
<div class="line"><a id="l00025" name="l00025"></a><span class="lineno">   25</span>            output = std::make_shared&lt;Tensor&gt;(shape, requires_grad);</div>
<div class="line"><a id="l00026" name="l00026"></a><span class="lineno">   26</span>            type = <span class="stringliteral">&quot;Input&quot;</span>;</div>
<div class="line"><a id="l00027" name="l00027"></a><span class="lineno">   27</span>        }</div>
</div>
<div class="line"><a id="l00028" name="l00028"></a><span class="lineno">   28</span> </div>
<div class="foldopen" id="foldopen00029" data-start="{" data-end="}">
<div class="line"><a id="l00029" name="l00029"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1io_1_1_input_node.html#ad3c9b52eaaff63ea2e47bf5bea6e342c">   29</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1io_1_1_input_node.html#a217dbf39ca3882f5e514357f72f29458">InputNode::InputNode</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_tensor.html">Tensor</a>&amp; tensor) {</div>
<div class="line"><a id="l00030" name="l00030"></a><span class="lineno">   30</span>            output = std::make_shared&lt;Tensor&gt;(tensor);</div>
<div class="line"><a id="l00031" name="l00031"></a><span class="lineno">   31</span>            type = <span class="stringliteral">&quot;Input&quot;</span>;</div>
<div class="line"><a id="l00032" name="l00032"></a><span class="lineno">   32</span>        }</div>
</div>
<div class="line"><a id="l00033" name="l00033"></a><span class="lineno">   33</span> </div>
<div class="foldopen" id="foldopen00034" data-start="{" data-end="}">
<div class="line"><a id="l00034" name="l00034"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1io_1_1_input_node.html#a7092655e4fa2edee102018c327aa4995">   34</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1io_1_1_input_node.html#a217dbf39ca3882f5e514357f72f29458">InputNode::InputNode</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">Tensor::shape_type</a>&amp; shape, Tensor::value_type* data, <span class="keyword">const</span> <span class="keywordtype">bool</span> requires_grad,</div>
<div class="line"><a id="l00035" name="l00035"></a><span class="lineno">   35</span>                             <span class="keywordtype">bool</span> host) {</div>
<div class="line"><a id="l00036" name="l00036"></a><span class="lineno">   36</span>            output = std::make_shared&lt;Tensor&gt;(shape, data, requires_grad, host);</div>
<div class="line"><a id="l00037" name="l00037"></a><span class="lineno">   37</span>            type = <span class="stringliteral">&quot;Input&quot;</span>;</div>
<div class="line"><a id="l00038" name="l00038"></a><span class="lineno">   38</span>        }</div>
</div>
<div class="line"><a id="l00039" name="l00039"></a><span class="lineno">   39</span> </div>
<div class="foldopen" id="foldopen00040" data-start="{" data-end="}">
<div class="line"><a id="l00040" name="l00040"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1io_1_1_input_node.html#a39a051c8a6b250b024fbd98feb959c63">   40</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1io_1_1_input_node.html#a217dbf39ca3882f5e514357f72f29458">InputNode::InputNode</a>(<span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">Tensor::shape_type</a>&amp; shape, <span class="keyword">const</span> std::initializer_list&lt;Tensor::value_type&gt;&amp; data,</div>
<div class="line"><a id="l00041" name="l00041"></a><span class="lineno">   41</span>                             <span class="keyword">const</span> <span class="keywordtype">bool</span> requires_grad) {</div>
<div class="line"><a id="l00042" name="l00042"></a><span class="lineno">   42</span>            output = std::make_shared&lt;Tensor&gt;(shape, data, requires_grad);</div>
<div class="line"><a id="l00043" name="l00043"></a><span class="lineno">   43</span>            type = <span class="stringliteral">&quot;Input&quot;</span>;</div>
<div class="line"><a id="l00044" name="l00044"></a><span class="lineno">   44</span>        }</div>
</div>
<div class="line"><a id="l00045" name="l00045"></a><span class="lineno">   45</span> </div>
<div class="foldopen" id="foldopen00046" data-start="{" data-end="}">
<div class="line"><a id="l00046" name="l00046"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1io_1_1_input_node.html#a4ba34603676c094723409d9e6b770976">   46</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1io_1_1_input_node.html#a4ba34603676c094723409d9e6b770976">InputNode::forward</a>() {</div>
<div class="line"><a id="l00047" name="l00047"></a><span class="lineno">   47</span>        }</div>
</div>
<div class="line"><a id="l00048" name="l00048"></a><span class="lineno">   48</span> </div>
<div class="foldopen" id="foldopen00049" data-start="{" data-end="}">
<div class="line"><a id="l00049" name="l00049"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1io_1_1_input_node.html#a3cde8af9401a117601dcdb0c9063516a">   49</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1io_1_1_input_node.html#a3cde8af9401a117601dcdb0c9063516a">InputNode::backward</a>() {</div>
<div class="line"><a id="l00050" name="l00050"></a><span class="lineno">   50</span>        }</div>
</div>
<div class="line"><a id="l00051" name="l00051"></a><span class="lineno">   51</span> </div>
<div class="foldopen" id="foldopen00052" data-start="{" data-end="}">
<div class="line"><a id="l00052" name="l00052"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1io_1_1_output_node.html#a98af165dc12d16d812708c3cdc9097b2">   52</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1io_1_1_output_node.html#a98af165dc12d16d812708c3cdc9097b2">OutputNode::OutputNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input) {</div>
<div class="line"><a id="l00053" name="l00053"></a><span class="lineno">   53</span>            loss = 0;</div>
<div class="line"><a id="l00054" name="l00054"></a><span class="lineno">   54</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00055" name="l00055"></a><span class="lineno">   55</span>            type = <span class="stringliteral">&quot;Output&quot;</span>;</div>
<div class="line"><a id="l00056" name="l00056"></a><span class="lineno">   56</span>        }</div>
</div>
<div class="line"><a id="l00057" name="l00057"></a><span class="lineno">   57</span> </div>
<div class="foldopen" id="foldopen00058" data-start="{" data-end="}">
<div class="line"><a id="l00058" name="l00058"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1io_1_1_output_node.html#a1c05ec6cdbddef105a20c400d0515471">   58</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1io_1_1_output_node.html#a1c05ec6cdbddef105a20c400d0515471">OutputNode::forward</a>() {</div>
<div class="line"><a id="l00059" name="l00059"></a><span class="lineno">   59</span>            output = inputs[0]-&gt;output;</div>
<div class="line"><a id="l00060" name="l00060"></a><span class="lineno">   60</span>        }</div>
</div>
<div class="line"><a id="l00061" name="l00061"></a><span class="lineno">   61</span> </div>
<div class="foldopen" id="foldopen00062" data-start="{" data-end="}">
<div class="line"><a id="l00062" name="l00062"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1io_1_1_output_node.html#a2f76355b646a9c9f1a0972ad87f6a260">   62</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1io_1_1_output_node.html#a2f76355b646a9c9f1a0972ad87f6a260">OutputNode::backward</a>() {</div>
<div class="line"><a id="l00063" name="l00063"></a><span class="lineno">   63</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00064" name="l00064"></a><span class="lineno">   64</span>                inputs[0]-&gt;output-&gt;fill(1, <span class="keyword">true</span>);</div>
<div class="line"><a id="l00065" name="l00065"></a><span class="lineno">   65</span>            }</div>
<div class="line"><a id="l00066" name="l00066"></a><span class="lineno">   66</span>        }</div>
</div>
<div class="line"><a id="l00067" name="l00067"></a><span class="lineno">   67</span> </div>
<div class="foldopen" id="foldopen00068" data-start="{" data-end="}">
<div class="line"><a id="l00068" name="l00068"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1io_1_1_output_node.html#a7ac1292b280afcd86b31853b1275c1c4">   68</a></span>        Tensor::value_type <a class="code hl_function" href="classnz_1_1nodes_1_1io_1_1_output_node.html#a7ac1292b280afcd86b31853b1275c1c4">OutputNode::getLoss</a>()<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00069" name="l00069"></a><span class="lineno">   69</span>            <span class="keywordflow">return</span> loss;</div>
<div class="line"><a id="l00070" name="l00070"></a><span class="lineno">   70</span>        }</div>
</div>
<div class="line"><a id="l00071" name="l00071"></a><span class="lineno">   71</span> </div>
<div class="foldopen" id="foldopen00072" data-start="{" data-end="}">
<div class="line"><a id="l00072" name="l00072"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1io_1_1_output_node.html#ac340bd5a932808333e08e8bf24d53039">   72</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1io_1_1_output_node.html#ac340bd5a932808333e08e8bf24d53039">OutputNode::print</a>(std::ostream&amp; os)<span class="keyword"> const </span>{</div>
<div class="line"><a id="l00073" name="l00073"></a><span class="lineno">   73</span>            <a class="code hl_function" href="classnz_1_1nodes_1_1_node.html#a687ee9c34eb61f8f28caa201ca42696e">Node::print</a>(os);</div>
<div class="line"><a id="l00074" name="l00074"></a><span class="lineno">   74</span>            os &lt;&lt; <span class="stringliteral">&quot;Loss: &quot;</span> &lt;&lt; loss &lt;&lt; std::endl;</div>
<div class="line"><a id="l00075" name="l00075"></a><span class="lineno">   75</span>        }</div>
</div>
<div class="line"><a id="l00076" name="l00076"></a><span class="lineno">   76</span>    }</div>
<div class="line"><a id="l00077" name="l00077"></a><span class="lineno">   77</span> </div>
<div class="line"><a id="l00078" name="l00078"></a><span class="lineno">   78</span>    <span class="keyword">namespace </span>calc {</div>
<div class="foldopen" id="foldopen00079" data-start="{" data-end="}">
<div class="line"><a id="l00079" name="l00079"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_add_node.html#abf5d0c2b9827bfb8fd1f3a004db80175">   79</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_add_node.html#abf5d0c2b9827bfb8fd1f3a004db80175">AddNode::AddNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input_left, <a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input_right) {</div>
<div class="line"><a id="l00080" name="l00080"></a><span class="lineno">   80</span>            <span class="keywordflow">if</span> (!input_left-&gt;output-&gt;shape().isBroadcastCompatible(input_right-&gt;output-&gt;shape()) || input_left-&gt;output-&gt;</div>
<div class="line"><a id="l00081" name="l00081"></a><span class="lineno">   81</span>                shape().H() != input_right-&gt;output-&gt;shape().H() || input_left-&gt;output-&gt;shape().W()</div>
<div class="line"><a id="l00082" name="l00082"></a><span class="lineno">   82</span>                !=</div>
<div class="line"><a id="l00083" name="l00083"></a><span class="lineno">   83</span>                input_right-&gt;output-&gt;shape().</div>
<div class="line"><a id="l00084" name="l00084"></a><span class="lineno">   84</span>                             W()) {</div>
<div class="line"><a id="l00085" name="l00085"></a><span class="lineno">   85</span>                <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Shapes are not broadcast compatible.&quot;</span>);</div>
<div class="line"><a id="l00086" name="l00086"></a><span class="lineno">   86</span>            }</div>
<div class="line"><a id="l00087" name="l00087"></a><span class="lineno">   87</span>            inputs.push_back(input_left);</div>
<div class="line"><a id="l00088" name="l00088"></a><span class="lineno">   88</span>            inputs.push_back(input_right);</div>
<div class="line"><a id="l00089" name="l00089"></a><span class="lineno">   89</span>            <span class="keywordtype">bool</span> requires_grad = input_left-&gt;output-&gt;requiresGrad() || input_right-&gt;output-&gt;requiresGrad();</div>
<div class="line"><a id="l00090" name="l00090"></a><span class="lineno">   90</span>            output = std::make_shared&lt;Tensor&gt;(input_left-&gt;output-&gt;shape().Broadcast(input_right-&gt;output-&gt;shape()),</div>
<div class="line"><a id="l00091" name="l00091"></a><span class="lineno">   91</span>                                              requires_grad);</div>
<div class="line"><a id="l00092" name="l00092"></a><span class="lineno">   92</span>            type = <span class="stringliteral">&quot;Add&quot;</span>;</div>
<div class="line"><a id="l00093" name="l00093"></a><span class="lineno">   93</span>        }</div>
</div>
<div class="line"><a id="l00094" name="l00094"></a><span class="lineno">   94</span> </div>
<div class="foldopen" id="foldopen00095" data-start="{" data-end="}">
<div class="line"><a id="l00095" name="l00095"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_add_node.html#adcbcffc97ede105ec64c7360377b9af3">   95</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_add_node.html#adcbcffc97ede105ec64c7360377b9af3">AddNode::forward</a>() {</div>
<div class="line"><a id="l00096" name="l00096"></a><span class="lineno">   96</span>            <a class="code hl_function" href="namespacenz_1_1data.html#a8cf4ac2437dd67698684169bebb225d4">tensorMatrixAdd</a>(*output, *inputs[0]-&gt;output, *inputs[1]-&gt;output);</div>
<div class="line"><a id="l00097" name="l00097"></a><span class="lineno">   97</span>        }</div>
</div>
<div class="line"><a id="l00098" name="l00098"></a><span class="lineno">   98</span> </div>
<div class="foldopen" id="foldopen00099" data-start="{" data-end="}">
<div class="line"><a id="l00099" name="l00099"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_add_node.html#aacd0de4600132791c8da7860dba3e43c">   99</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_add_node.html#aacd0de4600132791c8da7860dba3e43c">AddNode::backward</a>() {</div>
<div class="line"><a id="l00100" name="l00100"></a><span class="lineno">  100</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00101" name="l00101"></a><span class="lineno">  101</span>                <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;shape() == output-&gt;shape()) {</div>
<div class="line"><a id="l00102" name="l00102"></a><span class="lineno">  102</span>                    <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;Tensor::value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(</div>
<div class="line"><a id="l00103" name="l00103"></a><span class="lineno">  103</span>                        inputs[0]-&gt;output-&gt;grad(), output-&gt;grad(), output-&gt;size() * <span class="keyword">sizeof</span>(Tensor::value_type),</div>
<div class="line"><a id="l00104" name="l00104"></a><span class="lineno">  104</span>                        cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00105" name="l00105"></a><span class="lineno">  105</span>                }</div>
<div class="line"><a id="l00106" name="l00106"></a><span class="lineno">  106</span>                <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00107" name="l00107"></a><span class="lineno">  107</span>                    <span class="keyword">const</span> dim3 block(BLOCKSIZE);</div>
<div class="line"><a id="l00108" name="l00108"></a><span class="lineno">  108</span>                    <span class="keyword">const</span> dim3 grid((output-&gt;shape()[2] * output-&gt;shape()[3] + BLOCKSIZE - 1) / BLOCKSIZE);</div>
<div class="line"><a id="l00109" name="l00109"></a><span class="lineno">  109</span>                    std::vector&lt;size_t&gt; offset_o;</div>
<div class="line"><a id="l00110" name="l00110"></a><span class="lineno">  110</span>                    std::vector&lt;size_t&gt; offset_i;</div>
<div class="line"><a id="l00111" name="l00111"></a><span class="lineno">  111</span>                    <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; output-&gt;shape()[0]; i++) {</div>
<div class="line"><a id="l00112" name="l00112"></a><span class="lineno">  112</span>                        <span class="keywordflow">for</span> (<span class="keyword">auto</span> j = 0; j &lt; output-&gt;shape()[1]; j++) {</div>
<div class="line"><a id="l00113" name="l00113"></a><span class="lineno">  113</span>                            offset_i.push_back(i * output-&gt;shape().getStride(0) + j * output-&gt;shape().getStride(1));</div>
<div class="line"><a id="l00114" name="l00114"></a><span class="lineno">  114</span>                            offset_o.push_back(</div>
<div class="line"><a id="l00115" name="l00115"></a><span class="lineno">  115</span>                                i * (inputs[0]-&gt;output-&gt;shape()[0] &gt; 1 ? inputs[0]-&gt;output-&gt;shape().getStride(0) : 0) +</div>
<div class="line"><a id="l00116" name="l00116"></a><span class="lineno">  116</span>                                j * (inputs[0]-&gt;output-&gt;shape()[1] &gt; 1 ? inputs[0]-&gt;output-&gt;shape().getStride(1) : 0));</div>
<div class="line"><a id="l00117" name="l00117"></a><span class="lineno">  117</span>                        }</div>
<div class="line"><a id="l00118" name="l00118"></a><span class="lineno">  118</span>                    }</div>
<div class="line"><a id="l00119" name="l00119"></a><span class="lineno">  119</span>                    <a class="code hl_function" href="namespacenz_1_1krnl.html#a0ed44a68bfb86a9fd3d6c3b25614713f">gradCopy</a>(grid, block, inputs[0]-&gt;output-&gt;grad(), output-&gt;grad(),</div>
<div class="line"><a id="l00120" name="l00120"></a><span class="lineno">  120</span>                             output-&gt;shape()[2] * output-&gt;shape()[3], offset_o, offset_i);</div>
<div class="line"><a id="l00121" name="l00121"></a><span class="lineno">  121</span>                }</div>
<div class="line"><a id="l00122" name="l00122"></a><span class="lineno">  122</span>            }</div>
<div class="line"><a id="l00123" name="l00123"></a><span class="lineno">  123</span>            <span class="keywordflow">if</span> (inputs[1]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00124" name="l00124"></a><span class="lineno">  124</span>                <span class="keywordflow">if</span> (inputs[1]-&gt;output-&gt;shape() == output-&gt;shape()) {</div>
<div class="line"><a id="l00125" name="l00125"></a><span class="lineno">  125</span>                    <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;Tensor::value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(</div>
<div class="line"><a id="l00126" name="l00126"></a><span class="lineno">  126</span>                        inputs[1]-&gt;output-&gt;grad(), output-&gt;grad(), output-&gt;size() * <span class="keyword">sizeof</span>(Tensor::value_type),</div>
<div class="line"><a id="l00127" name="l00127"></a><span class="lineno">  127</span>                        cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00128" name="l00128"></a><span class="lineno">  128</span>                }</div>
<div class="line"><a id="l00129" name="l00129"></a><span class="lineno">  129</span>                <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00130" name="l00130"></a><span class="lineno">  130</span>                    <span class="keyword">const</span> dim3 block(BLOCKSIZE);</div>
<div class="line"><a id="l00131" name="l00131"></a><span class="lineno">  131</span>                    <span class="keyword">const</span> dim3 grid((output-&gt;shape()[2] * output-&gt;shape()[3] + BLOCKSIZE - 1) / BLOCKSIZE);</div>
<div class="line"><a id="l00132" name="l00132"></a><span class="lineno">  132</span>                    std::vector&lt;size_t&gt; offset_o;</div>
<div class="line"><a id="l00133" name="l00133"></a><span class="lineno">  133</span>                    std::vector&lt;size_t&gt; offset_i;</div>
<div class="line"><a id="l00134" name="l00134"></a><span class="lineno">  134</span>                    <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; output-&gt;shape()[0]; i++) {</div>
<div class="line"><a id="l00135" name="l00135"></a><span class="lineno">  135</span>                        <span class="keywordflow">for</span> (<span class="keyword">auto</span> j = 0; j &lt; output-&gt;shape()[1]; j++) {</div>
<div class="line"><a id="l00136" name="l00136"></a><span class="lineno">  136</span>                            offset_i.push_back(i * output-&gt;shape().getStride(0) + j * output-&gt;shape().getStride(1));</div>
<div class="line"><a id="l00137" name="l00137"></a><span class="lineno">  137</span>                            offset_o.push_back(</div>
<div class="line"><a id="l00138" name="l00138"></a><span class="lineno">  138</span>                                i * (inputs[1]-&gt;output-&gt;shape()[0] &gt; 1 ? inputs[1]-&gt;output-&gt;shape().getStride(0) : 0) +</div>
<div class="line"><a id="l00139" name="l00139"></a><span class="lineno">  139</span>                                j * (inputs[1]-&gt;output-&gt;shape()[1] &gt; 1 ? inputs[1]-&gt;output-&gt;shape().getStride(1) : 0));</div>
<div class="line"><a id="l00140" name="l00140"></a><span class="lineno">  140</span>                        }</div>
<div class="line"><a id="l00141" name="l00141"></a><span class="lineno">  141</span>                    }</div>
<div class="line"><a id="l00142" name="l00142"></a><span class="lineno">  142</span>                    <a class="code hl_function" href="namespacenz_1_1krnl.html#a0ed44a68bfb86a9fd3d6c3b25614713f">gradCopy</a>(grid, block, inputs[1]-&gt;output-&gt;grad(), output-&gt;grad(),</div>
<div class="line"><a id="l00143" name="l00143"></a><span class="lineno">  143</span>                             output-&gt;shape()[2] * output-&gt;shape()[3], offset_o, offset_i);</div>
<div class="line"><a id="l00144" name="l00144"></a><span class="lineno">  144</span>                }</div>
<div class="line"><a id="l00145" name="l00145"></a><span class="lineno">  145</span>            }</div>
<div class="line"><a id="l00146" name="l00146"></a><span class="lineno">  146</span>        }</div>
</div>
<div class="line"><a id="l00147" name="l00147"></a><span class="lineno">  147</span> </div>
<div class="foldopen" id="foldopen00148" data-start="{" data-end="}">
<div class="line"><a id="l00148" name="l00148"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_mat_mul_node.html#a493c723e88870bdc46c9b30b74b1e173">  148</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_mat_mul_node.html#a493c723e88870bdc46c9b30b74b1e173">MatMulNode::MatMulNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input_left, <a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input_right) {</div>
<div class="line"><a id="l00149" name="l00149"></a><span class="lineno">  149</span>            <span class="keywordflow">if</span> (!input_left-&gt;output-&gt;shape().isBroadcastCompatible(input_right-&gt;output-&gt;shape()) || input_left-&gt;output-&gt;</div>
<div class="line"><a id="l00150" name="l00150"></a><span class="lineno">  150</span>                shape().W() != input_right-&gt;output-&gt;shape().H()) {</div>
<div class="line"><a id="l00151" name="l00151"></a><span class="lineno">  151</span>                <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Shapes are not broadcast compatible.&quot;</span>);</div>
<div class="line"><a id="l00152" name="l00152"></a><span class="lineno">  152</span>            }</div>
<div class="line"><a id="l00153" name="l00153"></a><span class="lineno">  153</span>            inputs.push_back(input_left);</div>
<div class="line"><a id="l00154" name="l00154"></a><span class="lineno">  154</span>            inputs.push_back(input_right);</div>
<div class="line"><a id="l00155" name="l00155"></a><span class="lineno">  155</span>            <span class="keywordtype">bool</span> requires_grad = input_left-&gt;output-&gt;requiresGrad() || input_right-&gt;output-&gt;requiresGrad();</div>
<div class="line"><a id="l00156" name="l00156"></a><span class="lineno">  156</span>            <a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">Tensor::shape_type</a> shape = {</div>
<div class="line"><a id="l00157" name="l00157"></a><span class="lineno">  157</span>                std::max(input_left-&gt;output-&gt;shape()[0], input_right-&gt;output-&gt;shape()[0]),</div>
<div class="line"><a id="l00158" name="l00158"></a><span class="lineno">  158</span>                std::max(input_left-&gt;output-&gt;shape()[1], input_right-&gt;output-&gt;shape()[1]),</div>
<div class="line"><a id="l00159" name="l00159"></a><span class="lineno">  159</span>                input_left-&gt;output-&gt;shape()[2], input_right-&gt;output-&gt;shape()[3]</div>
<div class="line"><a id="l00160" name="l00160"></a><span class="lineno">  160</span>            };</div>
<div class="line"><a id="l00161" name="l00161"></a><span class="lineno">  161</span>            output = std::make_shared&lt;Tensor&gt;(shape, requires_grad);</div>
<div class="line"><a id="l00162" name="l00162"></a><span class="lineno">  162</span>            type = <span class="stringliteral">&quot;MatMul&quot;</span>;</div>
<div class="line"><a id="l00163" name="l00163"></a><span class="lineno">  163</span>        }</div>
</div>
<div class="line"><a id="l00164" name="l00164"></a><span class="lineno">  164</span> </div>
<div class="foldopen" id="foldopen00165" data-start="{" data-end="}">
<div class="line"><a id="l00165" name="l00165"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_mat_mul_node.html#a4d1ec1a90036ff16358c5f83123bac67">  165</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_mat_mul_node.html#a4d1ec1a90036ff16358c5f83123bac67">MatMulNode::forward</a>() {</div>
<div class="line"><a id="l00166" name="l00166"></a><span class="lineno">  166</span>            GEMMTensorCore(*output, *inputs[0]-&gt;output, *inputs[1]-&gt;output);</div>
<div class="line"><a id="l00167" name="l00167"></a><span class="lineno">  167</span>        }</div>
</div>
<div class="line"><a id="l00168" name="l00168"></a><span class="lineno">  168</span> </div>
<div class="foldopen" id="foldopen00169" data-start="{" data-end="}">
<div class="line"><a id="l00169" name="l00169"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_mat_mul_node.html#ab644a874feb6a620ad31d37ca20525fd">  169</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_mat_mul_node.html#ab644a874feb6a620ad31d37ca20525fd">MatMulNode::backward</a>() {</div>
<div class="line"><a id="l00170" name="l00170"></a><span class="lineno">  170</span>            <span class="comment">// dA = dC * B^T</span></div>
<div class="line"><a id="l00171" name="l00171"></a><span class="lineno">  171</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00172" name="l00172"></a><span class="lineno">  172</span>                <span class="keyword">auto</span> B_T = <a class="code hl_function" href="namespacenz_1_1data.html#ac8d64dd271e9a2e50682e733bd14ec19">transpose</a>(*inputs[1]-&gt;output);</div>
<div class="line"><a id="l00173" name="l00173"></a><span class="lineno">  173</span>                iGEMMBackward(output-&gt;grad(), B_T.data(), inputs[0]-&gt;output-&gt;grad(), output-&gt;shape(), B_T.shape(),</div>
<div class="line"><a id="l00174" name="l00174"></a><span class="lineno">  174</span>                              inputs[0]-&gt;output-&gt;shape());</div>
<div class="line"><a id="l00175" name="l00175"></a><span class="lineno">  175</span>            }</div>
<div class="line"><a id="l00176" name="l00176"></a><span class="lineno">  176</span>            <span class="comment">// dB = A^T * dC</span></div>
<div class="line"><a id="l00177" name="l00177"></a><span class="lineno">  177</span>            <span class="keywordflow">if</span> (inputs[1]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00178" name="l00178"></a><span class="lineno">  178</span>                <span class="keyword">auto</span> A_T = <a class="code hl_function" href="namespacenz_1_1data.html#ac8d64dd271e9a2e50682e733bd14ec19">transpose</a>(*inputs[0]-&gt;output);</div>
<div class="line"><a id="l00179" name="l00179"></a><span class="lineno">  179</span>                iGEMMBackward(A_T.data(), output-&gt;grad(), inputs[1]-&gt;output-&gt;grad(), A_T.shape(), output-&gt;shape(),</div>
<div class="line"><a id="l00180" name="l00180"></a><span class="lineno">  180</span>                              inputs[1]-&gt;output-&gt;shape());</div>
<div class="line"><a id="l00181" name="l00181"></a><span class="lineno">  181</span>            }</div>
<div class="line"><a id="l00182" name="l00182"></a><span class="lineno">  182</span>        }</div>
</div>
<div class="line"><a id="l00183" name="l00183"></a><span class="lineno">  183</span> </div>
<div class="foldopen" id="foldopen00184" data-start="{" data-end="}">
<div class="line"><a id="l00184" name="l00184"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_scalar_mul_node.html#a32ca702895991f74b867c06e1807c96e">  184</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_scalar_mul_node.html#a32ca702895991f74b867c06e1807c96e">ScalarMulNode::ScalarMulNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input, <span class="keyword">const</span> Tensor::value_type scalar) {</div>
<div class="line"><a id="l00185" name="l00185"></a><span class="lineno">  185</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00186" name="l00186"></a><span class="lineno">  186</span>            <span class="keywordtype">bool</span> requires_grad = input-&gt;output-&gt;requiresGrad();</div>
<div class="line"><a id="l00187" name="l00187"></a><span class="lineno">  187</span>            output = std::make_shared&lt;Tensor&gt;(input-&gt;output-&gt;shape(), requires_grad);</div>
<div class="line"><a id="l00188" name="l00188"></a><span class="lineno">  188</span>            this-&gt;scalar = scalar;</div>
<div class="line"><a id="l00189" name="l00189"></a><span class="lineno">  189</span>            type = <span class="stringliteral">&quot;ScalarMul&quot;</span>;</div>
<div class="line"><a id="l00190" name="l00190"></a><span class="lineno">  190</span>            WARN(</div>
<div class="line"><a id="l00191" name="l00191"></a><span class="lineno">  191</span>                <span class="stringliteral">&quot;Scalar operations do not yet support saving to files. If you want to save your model, consider using matrix operations instead.&quot;</span>);</div>
<div class="line"><a id="l00192" name="l00192"></a><span class="lineno">  192</span>        }</div>
</div>
<div class="line"><a id="l00193" name="l00193"></a><span class="lineno">  193</span> </div>
<div class="foldopen" id="foldopen00194" data-start="{" data-end="}">
<div class="line"><a id="l00194" name="l00194"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_scalar_mul_node.html#af96c94d5a91e2ee3bd97113992c829ca">  194</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_scalar_mul_node.html#af96c94d5a91e2ee3bd97113992c829ca">ScalarMulNode::forward</a>() {</div>
<div class="line"><a id="l00195" name="l00195"></a><span class="lineno">  195</span>            dim3 block(256);</div>
<div class="line"><a id="l00196" name="l00196"></a><span class="lineno">  196</span>            dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00197" name="l00197"></a><span class="lineno">  197</span>            <a class="code hl_function" href="namespacenz_1_1krnl.html#a5af716524e248c61f3dce227d8ef6e34">ScalarMul</a>(grid, block, output-&gt;data(), inputs[0]-&gt;output-&gt;data(), scalar, output-&gt;size());</div>
<div class="line"><a id="l00198" name="l00198"></a><span class="lineno">  198</span>        }</div>
</div>
<div class="line"><a id="l00199" name="l00199"></a><span class="lineno">  199</span> </div>
<div class="foldopen" id="foldopen00200" data-start="{" data-end="}">
<div class="line"><a id="l00200" name="l00200"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_scalar_mul_node.html#a074fe78c03e0b62c5e69b6a25b6b4c24">  200</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_scalar_mul_node.html#a074fe78c03e0b62c5e69b6a25b6b4c24">ScalarMulNode::backward</a>() {</div>
<div class="line"><a id="l00201" name="l00201"></a><span class="lineno">  201</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00202" name="l00202"></a><span class="lineno">  202</span>                dim3 block(256);</div>
<div class="line"><a id="l00203" name="l00203"></a><span class="lineno">  203</span>                dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00204" name="l00204"></a><span class="lineno">  204</span>                <a class="code hl_function" href="namespacenz_1_1krnl.html#a5af716524e248c61f3dce227d8ef6e34">ScalarMul</a>(grid, block, inputs[0]-&gt;output-&gt;grad(), output-&gt;grad(), scalar, output-&gt;size());</div>
<div class="line"><a id="l00205" name="l00205"></a><span class="lineno">  205</span>            }</div>
<div class="line"><a id="l00206" name="l00206"></a><span class="lineno">  206</span>        }</div>
</div>
<div class="line"><a id="l00207" name="l00207"></a><span class="lineno">  207</span> </div>
<div class="foldopen" id="foldopen00208" data-start="{" data-end="}">
<div class="line"><a id="l00208" name="l00208"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_scalar_div_node.html#ac04d8d6de4becf4e1c7911e99c131b7d">  208</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_scalar_div_node.html#ac04d8d6de4becf4e1c7911e99c131b7d">ScalarDivNode::ScalarDivNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input, <span class="keyword">const</span> Tensor::value_type scalar) {</div>
<div class="line"><a id="l00209" name="l00209"></a><span class="lineno">  209</span>            <span class="keywordflow">if</span> (scalar == 0) {</div>
<div class="line"><a id="l00210" name="l00210"></a><span class="lineno">  210</span>                <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;scalar cannot be zero&quot;</span>);</div>
<div class="line"><a id="l00211" name="l00211"></a><span class="lineno">  211</span>            }</div>
<div class="line"><a id="l00212" name="l00212"></a><span class="lineno">  212</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00213" name="l00213"></a><span class="lineno">  213</span>            <span class="keywordtype">bool</span> requires_grad = input-&gt;output-&gt;requiresGrad();</div>
<div class="line"><a id="l00214" name="l00214"></a><span class="lineno">  214</span>            output = std::make_shared&lt;Tensor&gt;(input-&gt;output-&gt;shape(), requires_grad);</div>
<div class="line"><a id="l00215" name="l00215"></a><span class="lineno">  215</span>            this-&gt;scalar = scalar;</div>
<div class="line"><a id="l00216" name="l00216"></a><span class="lineno">  216</span>            type = <span class="stringliteral">&quot;ScalarDiv&quot;</span>;</div>
<div class="line"><a id="l00217" name="l00217"></a><span class="lineno">  217</span>            WARN(</div>
<div class="line"><a id="l00218" name="l00218"></a><span class="lineno">  218</span>                <span class="stringliteral">&quot;Scalar operations do not yet support saving to files. If you want to save your model, consider using matrix operations instead.&quot;</span>);</div>
<div class="line"><a id="l00219" name="l00219"></a><span class="lineno">  219</span>        }</div>
</div>
<div class="line"><a id="l00220" name="l00220"></a><span class="lineno">  220</span> </div>
<div class="foldopen" id="foldopen00221" data-start="{" data-end="}">
<div class="line"><a id="l00221" name="l00221"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_scalar_div_node.html#a4728d1f10d35d7e71b11acd32ee1a26d">  221</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_scalar_div_node.html#a4728d1f10d35d7e71b11acd32ee1a26d">ScalarDivNode::forward</a>() {</div>
<div class="line"><a id="l00222" name="l00222"></a><span class="lineno">  222</span>            dim3 block(256);</div>
<div class="line"><a id="l00223" name="l00223"></a><span class="lineno">  223</span>            dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00224" name="l00224"></a><span class="lineno">  224</span>            <a class="code hl_function" href="namespacenz_1_1krnl.html#a27bc4025be4253d5fffae2bf1b43b3af">ScalarDiv</a>(grid, block, output-&gt;data(), inputs[0]-&gt;output-&gt;data(), scalar, output-&gt;size());</div>
<div class="line"><a id="l00225" name="l00225"></a><span class="lineno">  225</span>        }</div>
</div>
<div class="line"><a id="l00226" name="l00226"></a><span class="lineno">  226</span> </div>
<div class="foldopen" id="foldopen00227" data-start="{" data-end="}">
<div class="line"><a id="l00227" name="l00227"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_scalar_div_node.html#a9b72dc5618e8e11790756c91116719e4">  227</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_scalar_div_node.html#a9b72dc5618e8e11790756c91116719e4">ScalarDivNode::backward</a>() {</div>
<div class="line"><a id="l00228" name="l00228"></a><span class="lineno">  228</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00229" name="l00229"></a><span class="lineno">  229</span>                dim3 block(256);</div>
<div class="line"><a id="l00230" name="l00230"></a><span class="lineno">  230</span>                dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00231" name="l00231"></a><span class="lineno">  231</span>                <a class="code hl_function" href="namespacenz_1_1krnl.html#a27bc4025be4253d5fffae2bf1b43b3af">ScalarDiv</a>(grid, block, inputs[0]-&gt;output-&gt;grad(), output-&gt;grad(), scalar, output-&gt;size());</div>
<div class="line"><a id="l00232" name="l00232"></a><span class="lineno">  232</span>            }</div>
<div class="line"><a id="l00233" name="l00233"></a><span class="lineno">  233</span>        }</div>
</div>
<div class="line"><a id="l00234" name="l00234"></a><span class="lineno">  234</span> </div>
<div class="foldopen" id="foldopen00235" data-start="{" data-end="}">
<div class="line"><a id="l00235" name="l00235"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_scalar_add_node.html#a83edb10337111d0ffe7140a154954a3b">  235</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_scalar_add_node.html#a83edb10337111d0ffe7140a154954a3b">ScalarAddNode::ScalarAddNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input, <span class="keyword">const</span> Tensor::value_type scalar) {</div>
<div class="line"><a id="l00236" name="l00236"></a><span class="lineno">  236</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00237" name="l00237"></a><span class="lineno">  237</span>            <span class="keywordtype">bool</span> requires_grad = input-&gt;output-&gt;requiresGrad();</div>
<div class="line"><a id="l00238" name="l00238"></a><span class="lineno">  238</span>            output = std::make_shared&lt;Tensor&gt;(input-&gt;output-&gt;shape(), requires_grad);</div>
<div class="line"><a id="l00239" name="l00239"></a><span class="lineno">  239</span>            this-&gt;scalar = scalar;</div>
<div class="line"><a id="l00240" name="l00240"></a><span class="lineno">  240</span>            type = <span class="stringliteral">&quot;ScalarAdd&quot;</span>;</div>
<div class="line"><a id="l00241" name="l00241"></a><span class="lineno">  241</span>            WARN(</div>
<div class="line"><a id="l00242" name="l00242"></a><span class="lineno">  242</span>                <span class="stringliteral">&quot;Scalar operations do not yet support saving to files. If you want to save your model, consider using matrix operations instead.&quot;</span>);</div>
<div class="line"><a id="l00243" name="l00243"></a><span class="lineno">  243</span>        }</div>
</div>
<div class="line"><a id="l00244" name="l00244"></a><span class="lineno">  244</span> </div>
<div class="foldopen" id="foldopen00245" data-start="{" data-end="}">
<div class="line"><a id="l00245" name="l00245"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_scalar_add_node.html#ad1162e693cd13ee6e9e4f7cab27e4a31">  245</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_scalar_add_node.html#ad1162e693cd13ee6e9e4f7cab27e4a31">ScalarAddNode::forward</a>() {</div>
<div class="line"><a id="l00246" name="l00246"></a><span class="lineno">  246</span>            dim3 block(256);</div>
<div class="line"><a id="l00247" name="l00247"></a><span class="lineno">  247</span>            dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00248" name="l00248"></a><span class="lineno">  248</span>            <a class="code hl_function" href="namespacenz_1_1krnl.html#a56f84e531825be8b2b0974c2488eb765">ScalarAdd</a>(grid, block, output-&gt;data(), inputs[0]-&gt;output-&gt;data(), scalar, output-&gt;size());</div>
<div class="line"><a id="l00249" name="l00249"></a><span class="lineno">  249</span>        }</div>
</div>
<div class="line"><a id="l00250" name="l00250"></a><span class="lineno">  250</span> </div>
<div class="foldopen" id="foldopen00251" data-start="{" data-end="}">
<div class="line"><a id="l00251" name="l00251"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_scalar_add_node.html#a453eed787a8161b36410bef2ba8b0a75">  251</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_scalar_add_node.html#a453eed787a8161b36410bef2ba8b0a75">ScalarAddNode::backward</a>() {</div>
<div class="line"><a id="l00252" name="l00252"></a><span class="lineno">  252</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00253" name="l00253"></a><span class="lineno">  253</span>                <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;Tensor::value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(</div>
<div class="line"><a id="l00254" name="l00254"></a><span class="lineno">  254</span>                    inputs[0]-&gt;output-&gt;grad(), output-&gt;grad(), output-&gt;size() * <span class="keyword">sizeof</span>(Tensor::value_type),</div>
<div class="line"><a id="l00255" name="l00255"></a><span class="lineno">  255</span>                    cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00256" name="l00256"></a><span class="lineno">  256</span>            }</div>
<div class="line"><a id="l00257" name="l00257"></a><span class="lineno">  257</span>        }</div>
</div>
<div class="line"><a id="l00258" name="l00258"></a><span class="lineno">  258</span> </div>
<div class="foldopen" id="foldopen00259" data-start="{" data-end="}">
<div class="line"><a id="l00259" name="l00259"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_scalar_sub_node.html#a27b61f5a960545b810cf3151fe65adf6">  259</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_scalar_sub_node.html#a27b61f5a960545b810cf3151fe65adf6">ScalarSubNode::ScalarSubNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input, <span class="keyword">const</span> Tensor::value_type scalar) {</div>
<div class="line"><a id="l00260" name="l00260"></a><span class="lineno">  260</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00261" name="l00261"></a><span class="lineno">  261</span>            <span class="keywordtype">bool</span> requires_grad = input-&gt;output-&gt;requiresGrad();</div>
<div class="line"><a id="l00262" name="l00262"></a><span class="lineno">  262</span>            output = std::make_shared&lt;Tensor&gt;(input-&gt;output-&gt;shape(), requires_grad);</div>
<div class="line"><a id="l00263" name="l00263"></a><span class="lineno">  263</span>            this-&gt;scalar = -scalar;</div>
<div class="line"><a id="l00264" name="l00264"></a><span class="lineno">  264</span>            type = <span class="stringliteral">&quot;ScalarSub&quot;</span>;</div>
<div class="line"><a id="l00265" name="l00265"></a><span class="lineno">  265</span>            WARN(</div>
<div class="line"><a id="l00266" name="l00266"></a><span class="lineno">  266</span>                <span class="stringliteral">&quot;Scalar operations do not yet support saving to files. If you want to save your model, consider using matrix operations instead.&quot;</span>);</div>
<div class="line"><a id="l00267" name="l00267"></a><span class="lineno">  267</span>        }</div>
</div>
<div class="line"><a id="l00268" name="l00268"></a><span class="lineno">  268</span> </div>
<div class="foldopen" id="foldopen00269" data-start="{" data-end="}">
<div class="line"><a id="l00269" name="l00269"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_scalar_sub_node.html#ac5d375db4c17885e597c4dcca9d0a318">  269</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_scalar_sub_node.html#ac5d375db4c17885e597c4dcca9d0a318">ScalarSubNode::forward</a>() {</div>
<div class="line"><a id="l00270" name="l00270"></a><span class="lineno">  270</span>            dim3 block(256);</div>
<div class="line"><a id="l00271" name="l00271"></a><span class="lineno">  271</span>            dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00272" name="l00272"></a><span class="lineno">  272</span>            <a class="code hl_function" href="namespacenz_1_1krnl.html#a56f84e531825be8b2b0974c2488eb765">ScalarAdd</a>(grid, block, output-&gt;data(), inputs[0]-&gt;output-&gt;data(), scalar, output-&gt;size());</div>
<div class="line"><a id="l00273" name="l00273"></a><span class="lineno">  273</span>        }</div>
</div>
<div class="line"><a id="l00274" name="l00274"></a><span class="lineno">  274</span> </div>
<div class="foldopen" id="foldopen00275" data-start="{" data-end="}">
<div class="line"><a id="l00275" name="l00275"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_scalar_sub_node.html#a7880d18811e20c3ec34b1417a28d697e">  275</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_scalar_sub_node.html#a7880d18811e20c3ec34b1417a28d697e">ScalarSubNode::backward</a>() {</div>
<div class="line"><a id="l00276" name="l00276"></a><span class="lineno">  276</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00277" name="l00277"></a><span class="lineno">  277</span>                <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;Tensor::value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(</div>
<div class="line"><a id="l00278" name="l00278"></a><span class="lineno">  278</span>                    inputs[0]-&gt;output-&gt;grad(), output-&gt;grad(), output-&gt;size() * <span class="keyword">sizeof</span>(Tensor::value_type),</div>
<div class="line"><a id="l00279" name="l00279"></a><span class="lineno">  279</span>                    cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00280" name="l00280"></a><span class="lineno">  280</span>            }</div>
<div class="line"><a id="l00281" name="l00281"></a><span class="lineno">  281</span>        }</div>
</div>
<div class="line"><a id="l00282" name="l00282"></a><span class="lineno">  282</span> </div>
<div class="foldopen" id="foldopen00283" data-start="{" data-end="}">
<div class="line"><a id="l00283" name="l00283"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_sub_node.html#ab5c38bfc256e7784a22bb2bdcab7a72d">  283</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_sub_node.html#ab5c38bfc256e7784a22bb2bdcab7a72d">SubNode::SubNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input_left, <a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input_right) {</div>
<div class="line"><a id="l00284" name="l00284"></a><span class="lineno">  284</span>            <span class="keywordflow">if</span> (!input_left-&gt;output-&gt;shape().isBroadcastCompatible(input_right-&gt;output-&gt;shape()) || input_left-&gt;output-&gt;</div>
<div class="line"><a id="l00285" name="l00285"></a><span class="lineno">  285</span>                shape().H() != input_right-&gt;output-&gt;shape().H() || input_left-&gt;output-&gt;shape().W() !=</div>
<div class="line"><a id="l00286" name="l00286"></a><span class="lineno">  286</span>                input_right-&gt;output-&gt;shape().</div>
<div class="line"><a id="l00287" name="l00287"></a><span class="lineno">  287</span>                             W()) {</div>
<div class="line"><a id="l00288" name="l00288"></a><span class="lineno">  288</span>                <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;Shapes are not broadcast compatible.&quot;</span>);</div>
<div class="line"><a id="l00289" name="l00289"></a><span class="lineno">  289</span>            }</div>
<div class="line"><a id="l00290" name="l00290"></a><span class="lineno">  290</span>            inputs.push_back(input_left);</div>
<div class="line"><a id="l00291" name="l00291"></a><span class="lineno">  291</span>            inputs.push_back(input_right);</div>
<div class="line"><a id="l00292" name="l00292"></a><span class="lineno">  292</span>            <span class="keywordtype">bool</span> requires_grad = input_left-&gt;output-&gt;requiresGrad() || input_right-&gt;output-&gt;requiresGrad();</div>
<div class="line"><a id="l00293" name="l00293"></a><span class="lineno">  293</span>            output = std::make_shared&lt;Tensor&gt;(input_left-&gt;output-&gt;shape().Broadcast(input_right-&gt;output-&gt;shape()),</div>
<div class="line"><a id="l00294" name="l00294"></a><span class="lineno">  294</span>                                              requires_grad);</div>
<div class="line"><a id="l00295" name="l00295"></a><span class="lineno">  295</span>            type = <span class="stringliteral">&quot;Sub&quot;</span>;</div>
<div class="line"><a id="l00296" name="l00296"></a><span class="lineno">  296</span>        }</div>
</div>
<div class="line"><a id="l00297" name="l00297"></a><span class="lineno">  297</span> </div>
<div class="foldopen" id="foldopen00298" data-start="{" data-end="}">
<div class="line"><a id="l00298" name="l00298"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_sub_node.html#a6ba6a63da4e869f8f0004896d01fe3f1">  298</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_sub_node.html#a6ba6a63da4e869f8f0004896d01fe3f1">SubNode::forward</a>() {</div>
<div class="line"><a id="l00299" name="l00299"></a><span class="lineno">  299</span>            <a class="code hl_function" href="namespacenz_1_1data.html#a7503b6894e8052ed54eb169550d135c0">tensorMatrixSub</a>(*output, *inputs[0]-&gt;output, *inputs[1]-&gt;output);</div>
<div class="line"><a id="l00300" name="l00300"></a><span class="lineno">  300</span>        }</div>
</div>
<div class="line"><a id="l00301" name="l00301"></a><span class="lineno">  301</span> </div>
<div class="foldopen" id="foldopen00302" data-start="{" data-end="}">
<div class="line"><a id="l00302" name="l00302"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_sub_node.html#abb396a092ac9fb09c3a656329132842d">  302</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_sub_node.html#abb396a092ac9fb09c3a656329132842d">SubNode::backward</a>() {</div>
<div class="line"><a id="l00303" name="l00303"></a><span class="lineno">  303</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00304" name="l00304"></a><span class="lineno">  304</span>                <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;shape() == output-&gt;shape()) {</div>
<div class="line"><a id="l00305" name="l00305"></a><span class="lineno">  305</span>                    <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;Tensor::value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(</div>
<div class="line"><a id="l00306" name="l00306"></a><span class="lineno">  306</span>                        inputs[0]-&gt;output-&gt;grad(), output-&gt;grad(), output-&gt;size() * <span class="keyword">sizeof</span>(Tensor::value_type),</div>
<div class="line"><a id="l00307" name="l00307"></a><span class="lineno">  307</span>                        cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00308" name="l00308"></a><span class="lineno">  308</span>                }</div>
<div class="line"><a id="l00309" name="l00309"></a><span class="lineno">  309</span>                <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00310" name="l00310"></a><span class="lineno">  310</span>                    <span class="keyword">const</span> dim3 block(BLOCKSIZE);</div>
<div class="line"><a id="l00311" name="l00311"></a><span class="lineno">  311</span>                    <span class="keyword">const</span> dim3 grid((output-&gt;shape()[2] * output-&gt;shape()[3] + BLOCKSIZE - 1) / BLOCKSIZE);</div>
<div class="line"><a id="l00312" name="l00312"></a><span class="lineno">  312</span>                    std::vector&lt;size_t&gt; offset_o;</div>
<div class="line"><a id="l00313" name="l00313"></a><span class="lineno">  313</span>                    std::vector&lt;size_t&gt; offset_i;</div>
<div class="line"><a id="l00314" name="l00314"></a><span class="lineno">  314</span>                    <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; output-&gt;shape()[0]; i++) {</div>
<div class="line"><a id="l00315" name="l00315"></a><span class="lineno">  315</span>                        <span class="keywordflow">for</span> (<span class="keyword">auto</span> j = 0; j &lt; output-&gt;shape()[1]; j++) {</div>
<div class="line"><a id="l00316" name="l00316"></a><span class="lineno">  316</span>                            offset_i.push_back(i * output-&gt;shape().getStride(0) + j * output-&gt;shape().getStride(1));</div>
<div class="line"><a id="l00317" name="l00317"></a><span class="lineno">  317</span>                            offset_o.push_back(</div>
<div class="line"><a id="l00318" name="l00318"></a><span class="lineno">  318</span>                                i * (inputs[0]-&gt;output-&gt;shape()[0] &gt; 1 ? inputs[0]-&gt;output-&gt;shape().getStride(0) : 0) +</div>
<div class="line"><a id="l00319" name="l00319"></a><span class="lineno">  319</span>                                j * (inputs[0]-&gt;output-&gt;shape()[1] &gt; 1 ? inputs[0]-&gt;output-&gt;shape().getStride(1) : 0));</div>
<div class="line"><a id="l00320" name="l00320"></a><span class="lineno">  320</span>                        }</div>
<div class="line"><a id="l00321" name="l00321"></a><span class="lineno">  321</span>                    }</div>
<div class="line"><a id="l00322" name="l00322"></a><span class="lineno">  322</span>                    <a class="code hl_function" href="namespacenz_1_1krnl.html#a0ed44a68bfb86a9fd3d6c3b25614713f">gradCopy</a>(grid, block, inputs[0]-&gt;output-&gt;grad(), output-&gt;grad(),</div>
<div class="line"><a id="l00323" name="l00323"></a><span class="lineno">  323</span>                             output-&gt;shape()[2] * output-&gt;shape()[3], offset_o, offset_i);</div>
<div class="line"><a id="l00324" name="l00324"></a><span class="lineno">  324</span>                }</div>
<div class="line"><a id="l00325" name="l00325"></a><span class="lineno">  325</span>            }</div>
<div class="line"><a id="l00326" name="l00326"></a><span class="lineno">  326</span>            <span class="keywordflow">if</span> (inputs[1]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00327" name="l00327"></a><span class="lineno">  327</span>                <span class="keyword">const</span> dim3 block(BLOCKSIZE);</div>
<div class="line"><a id="l00328" name="l00328"></a><span class="lineno">  328</span>                <span class="keyword">const</span> dim3 grid((output-&gt;shape()[2] * output-&gt;shape()[3] + BLOCKSIZE - 1) / BLOCKSIZE);</div>
<div class="line"><a id="l00329" name="l00329"></a><span class="lineno">  329</span>                std::vector&lt;size_t&gt; offset_o;</div>
<div class="line"><a id="l00330" name="l00330"></a><span class="lineno">  330</span>                std::vector&lt;size_t&gt; offset_i;</div>
<div class="line"><a id="l00331" name="l00331"></a><span class="lineno">  331</span>                <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; output-&gt;shape()[0]; i++) {</div>
<div class="line"><a id="l00332" name="l00332"></a><span class="lineno">  332</span>                    <span class="keywordflow">for</span> (<span class="keyword">auto</span> j = 0; j &lt; output-&gt;shape()[1]; j++) {</div>
<div class="line"><a id="l00333" name="l00333"></a><span class="lineno">  333</span>                        offset_i.push_back(i * output-&gt;shape().getStride(0) + j * output-&gt;shape().getStride(1));</div>
<div class="line"><a id="l00334" name="l00334"></a><span class="lineno">  334</span>                        offset_o.push_back(</div>
<div class="line"><a id="l00335" name="l00335"></a><span class="lineno">  335</span>                            i * (inputs[1]-&gt;output-&gt;shape()[0] &gt; 1 ? inputs[1]-&gt;output-&gt;shape().getStride(0) : 0) +</div>
<div class="line"><a id="l00336" name="l00336"></a><span class="lineno">  336</span>                            j * (inputs[1]-&gt;output-&gt;shape()[1] &gt; 1 ? inputs[1]-&gt;output-&gt;shape().getStride(1) : 0));</div>
<div class="line"><a id="l00337" name="l00337"></a><span class="lineno">  337</span>                    }</div>
<div class="line"><a id="l00338" name="l00338"></a><span class="lineno">  338</span>                }</div>
<div class="line"><a id="l00339" name="l00339"></a><span class="lineno">  339</span>                <a class="code hl_function" href="namespacenz_1_1krnl.html#a9ac0590fbb5eb7f51b05da574e9845a8">NgradCopy</a>(grid, block, inputs[1]-&gt;output-&gt;grad(), output-&gt;grad(),</div>
<div class="line"><a id="l00340" name="l00340"></a><span class="lineno">  340</span>                          output-&gt;shape()[2] * output-&gt;shape()[3], offset_o, offset_i);</div>
<div class="line"><a id="l00341" name="l00341"></a><span class="lineno">  341</span>            }</div>
<div class="line"><a id="l00342" name="l00342"></a><span class="lineno">  342</span>        }</div>
</div>
<div class="line"><a id="l00343" name="l00343"></a><span class="lineno">  343</span> </div>
<div class="foldopen" id="foldopen00344" data-start="{" data-end="}">
<div class="line"><a id="l00344" name="l00344"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_re_l_u_node.html#a78a8e2e5cf13d31956e2367d923efa53">  344</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_re_l_u_node.html#a78a8e2e5cf13d31956e2367d923efa53">ReLUNode::ReLUNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input) {</div>
<div class="line"><a id="l00345" name="l00345"></a><span class="lineno">  345</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00346" name="l00346"></a><span class="lineno">  346</span>            <span class="keywordtype">bool</span> requires_grad = input-&gt;output-&gt;requiresGrad();</div>
<div class="line"><a id="l00347" name="l00347"></a><span class="lineno">  347</span>            output = std::make_shared&lt;Tensor&gt;(input-&gt;output-&gt;shape(), requires_grad);</div>
<div class="line"><a id="l00348" name="l00348"></a><span class="lineno">  348</span>            type = <span class="stringliteral">&quot;ReLU&quot;</span>;</div>
<div class="line"><a id="l00349" name="l00349"></a><span class="lineno">  349</span>        }</div>
</div>
<div class="line"><a id="l00350" name="l00350"></a><span class="lineno">  350</span> </div>
<div class="foldopen" id="foldopen00351" data-start="{" data-end="}">
<div class="line"><a id="l00351" name="l00351"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_re_l_u_node.html#ae8e78ee766f1e4845c6a110464e077f7">  351</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_re_l_u_node.html#ae8e78ee766f1e4845c6a110464e077f7">ReLUNode::forward</a>() {</div>
<div class="line"><a id="l00352" name="l00352"></a><span class="lineno">  352</span>            dim3 block(256);</div>
<div class="line"><a id="l00353" name="l00353"></a><span class="lineno">  353</span>            dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00354" name="l00354"></a><span class="lineno">  354</span>            <a class="code hl_function" href="namespacenz_1_1krnl.html#a8855f411733f7de29d013f4ad40096c9">RectifiedLinearUnit</a>(grid, block, output-&gt;data(), inputs[0]-&gt;output-&gt;data(), output-&gt;size());</div>
<div class="line"><a id="l00355" name="l00355"></a><span class="lineno">  355</span>        }</div>
</div>
<div class="line"><a id="l00356" name="l00356"></a><span class="lineno">  356</span> </div>
<div class="foldopen" id="foldopen00357" data-start="{" data-end="}">
<div class="line"><a id="l00357" name="l00357"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_re_l_u_node.html#af06dd1eadec4b3616c7c9e655820b1b8">  357</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_re_l_u_node.html#af06dd1eadec4b3616c7c9e655820b1b8">ReLUNode::backward</a>() {</div>
<div class="line"><a id="l00358" name="l00358"></a><span class="lineno">  358</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00359" name="l00359"></a><span class="lineno">  359</span>                dim3 block(256);</div>
<div class="line"><a id="l00360" name="l00360"></a><span class="lineno">  360</span>                dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00361" name="l00361"></a><span class="lineno">  361</span>                <a class="code hl_function" href="namespacenz_1_1krnl.html#a4ddfc808de99fe831e74a3bd3f9bbdaf">ReLUBackward</a>(grid, block, inputs[0]-&gt;output-&gt;grad(), inputs[0]-&gt;output-&gt;data(), output-&gt;grad(),</div>
<div class="line"><a id="l00362" name="l00362"></a><span class="lineno">  362</span>                             output-&gt;size());</div>
<div class="line"><a id="l00363" name="l00363"></a><span class="lineno">  363</span>            }</div>
<div class="line"><a id="l00364" name="l00364"></a><span class="lineno">  364</span>        }</div>
</div>
<div class="line"><a id="l00365" name="l00365"></a><span class="lineno">  365</span> </div>
<div class="foldopen" id="foldopen00366" data-start="{" data-end="}">
<div class="line"><a id="l00366" name="l00366"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_sigmoid_node.html#aad3fb8b89ac7cde6d70c464e06c35ade">  366</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_sigmoid_node.html#aad3fb8b89ac7cde6d70c464e06c35ade">SigmoidNode::SigmoidNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input) {</div>
<div class="line"><a id="l00367" name="l00367"></a><span class="lineno">  367</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00368" name="l00368"></a><span class="lineno">  368</span>            <span class="keywordtype">bool</span> requires_grad = input-&gt;output-&gt;requiresGrad();</div>
<div class="line"><a id="l00369" name="l00369"></a><span class="lineno">  369</span>            output = std::make_shared&lt;Tensor&gt;(input-&gt;output-&gt;shape(), requires_grad);</div>
<div class="line"><a id="l00370" name="l00370"></a><span class="lineno">  370</span>            type = <span class="stringliteral">&quot;Sigmoid&quot;</span>;</div>
<div class="line"><a id="l00371" name="l00371"></a><span class="lineno">  371</span>        }</div>
</div>
<div class="line"><a id="l00372" name="l00372"></a><span class="lineno">  372</span> </div>
<div class="foldopen" id="foldopen00373" data-start="{" data-end="}">
<div class="line"><a id="l00373" name="l00373"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_sigmoid_node.html#aa24b02f6d79fda31e6ad150879ed2bbb">  373</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_sigmoid_node.html#aa24b02f6d79fda31e6ad150879ed2bbb">SigmoidNode::forward</a>() {</div>
<div class="line"><a id="l00374" name="l00374"></a><span class="lineno">  374</span>            dim3 block(256);</div>
<div class="line"><a id="l00375" name="l00375"></a><span class="lineno">  375</span>            dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00376" name="l00376"></a><span class="lineno">  376</span>            <a class="code hl_function" href="namespacenz_1_1krnl.html#a21bbbcf6d97bfaccc828ce7736814bd4">Sigmoid</a>(grid, block, output-&gt;data(), inputs[0]-&gt;output-&gt;data(), output-&gt;size());</div>
<div class="line"><a id="l00377" name="l00377"></a><span class="lineno">  377</span>        }</div>
</div>
<div class="line"><a id="l00378" name="l00378"></a><span class="lineno">  378</span> </div>
<div class="foldopen" id="foldopen00379" data-start="{" data-end="}">
<div class="line"><a id="l00379" name="l00379"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_sigmoid_node.html#a05fe16b3ecde344d9463efabf1318115">  379</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_sigmoid_node.html#a05fe16b3ecde344d9463efabf1318115">SigmoidNode::backward</a>() {</div>
<div class="line"><a id="l00380" name="l00380"></a><span class="lineno">  380</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00381" name="l00381"></a><span class="lineno">  381</span>                dim3 block(256);</div>
<div class="line"><a id="l00382" name="l00382"></a><span class="lineno">  382</span>                dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00383" name="l00383"></a><span class="lineno">  383</span>                <a class="code hl_function" href="namespacenz_1_1krnl.html#aff1f9f1bf9fb677024bd2b565fab9801">SigmoidBackward</a>(grid, block, inputs[0]-&gt;output-&gt;grad(), output-&gt;data(), output-&gt;grad(), output-&gt;size());</div>
<div class="line"><a id="l00384" name="l00384"></a><span class="lineno">  384</span>            }</div>
<div class="line"><a id="l00385" name="l00385"></a><span class="lineno">  385</span>        }</div>
</div>
<div class="line"><a id="l00386" name="l00386"></a><span class="lineno">  386</span> </div>
<div class="foldopen" id="foldopen00387" data-start="{" data-end="}">
<div class="line"><a id="l00387" name="l00387"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_tanh_node.html#aea688a8ba028a288d331ed04d8fd4871">  387</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_tanh_node.html#aea688a8ba028a288d331ed04d8fd4871">TanhNode::TanhNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input) {</div>
<div class="line"><a id="l00388" name="l00388"></a><span class="lineno">  388</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00389" name="l00389"></a><span class="lineno">  389</span>            <span class="keywordtype">bool</span> requires_grad = input-&gt;output-&gt;requiresGrad();</div>
<div class="line"><a id="l00390" name="l00390"></a><span class="lineno">  390</span>            output = std::make_shared&lt;Tensor&gt;(input-&gt;output-&gt;shape(), requires_grad);</div>
<div class="line"><a id="l00391" name="l00391"></a><span class="lineno">  391</span>            type = <span class="stringliteral">&quot;Tanh&quot;</span>;</div>
<div class="line"><a id="l00392" name="l00392"></a><span class="lineno">  392</span>        }</div>
</div>
<div class="line"><a id="l00393" name="l00393"></a><span class="lineno">  393</span> </div>
<div class="foldopen" id="foldopen00394" data-start="{" data-end="}">
<div class="line"><a id="l00394" name="l00394"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_tanh_node.html#a451ff464932275955fbec1c33abdba97">  394</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_tanh_node.html#a451ff464932275955fbec1c33abdba97">TanhNode::forward</a>() {</div>
<div class="line"><a id="l00395" name="l00395"></a><span class="lineno">  395</span>            dim3 block(256);</div>
<div class="line"><a id="l00396" name="l00396"></a><span class="lineno">  396</span>            dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00397" name="l00397"></a><span class="lineno">  397</span>            <a class="code hl_function" href="namespacenz_1_1krnl.html#aeb7d10939b25508e0b5db1fe44f4b467">Tanh</a>(grid, block, output-&gt;data(), inputs[0]-&gt;output-&gt;data(), output-&gt;size());</div>
<div class="line"><a id="l00398" name="l00398"></a><span class="lineno">  398</span>        }</div>
</div>
<div class="line"><a id="l00399" name="l00399"></a><span class="lineno">  399</span> </div>
<div class="foldopen" id="foldopen00400" data-start="{" data-end="}">
<div class="line"><a id="l00400" name="l00400"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_tanh_node.html#ac75b18193ae5de920c0060ad83d1542a">  400</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_tanh_node.html#ac75b18193ae5de920c0060ad83d1542a">TanhNode::backward</a>() {</div>
<div class="line"><a id="l00401" name="l00401"></a><span class="lineno">  401</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00402" name="l00402"></a><span class="lineno">  402</span>                dim3 block(256);</div>
<div class="line"><a id="l00403" name="l00403"></a><span class="lineno">  403</span>                dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00404" name="l00404"></a><span class="lineno">  404</span>                <a class="code hl_function" href="namespacenz_1_1krnl.html#a90d501e72361b7341f36394af0f27c74">TanhBackward</a>(grid, block, inputs[0]-&gt;output-&gt;grad(), output-&gt;data(), output-&gt;grad(), output-&gt;size());</div>
<div class="line"><a id="l00405" name="l00405"></a><span class="lineno">  405</span>            }</div>
<div class="line"><a id="l00406" name="l00406"></a><span class="lineno">  406</span>        }</div>
</div>
<div class="line"><a id="l00407" name="l00407"></a><span class="lineno">  407</span> </div>
<div class="foldopen" id="foldopen00408" data-start="{" data-end="}">
<div class="line"><a id="l00408" name="l00408"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_leaky_re_l_u_node.html#ae26fe5bd4367d39c5b054ecd0f4621fa">  408</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_leaky_re_l_u_node.html#ae26fe5bd4367d39c5b054ecd0f4621fa">LeakyReLUNode::LeakyReLUNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input, Tensor::value_type alpha) {</div>
<div class="line"><a id="l00409" name="l00409"></a><span class="lineno">  409</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00410" name="l00410"></a><span class="lineno">  410</span>            <span class="keywordtype">bool</span> requires_grad = input-&gt;output-&gt;requiresGrad();</div>
<div class="line"><a id="l00411" name="l00411"></a><span class="lineno">  411</span>            output = std::make_shared&lt;Tensor&gt;(input-&gt;output-&gt;shape(), requires_grad);</div>
<div class="line"><a id="l00412" name="l00412"></a><span class="lineno">  412</span>            this-&gt;alpha = alpha;</div>
<div class="line"><a id="l00413" name="l00413"></a><span class="lineno">  413</span>            type = <span class="stringliteral">&quot;LeakyReLU&quot;</span>;</div>
<div class="line"><a id="l00414" name="l00414"></a><span class="lineno">  414</span>        }</div>
</div>
<div class="line"><a id="l00415" name="l00415"></a><span class="lineno">  415</span> </div>
<div class="foldopen" id="foldopen00416" data-start="{" data-end="}">
<div class="line"><a id="l00416" name="l00416"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_leaky_re_l_u_node.html#a287aebe2ce3437d393fb1ac1b1119d25">  416</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_leaky_re_l_u_node.html#a287aebe2ce3437d393fb1ac1b1119d25">LeakyReLUNode::forward</a>() {</div>
<div class="line"><a id="l00417" name="l00417"></a><span class="lineno">  417</span>            <span class="keyword">const</span> dim3 block(256);</div>
<div class="line"><a id="l00418" name="l00418"></a><span class="lineno">  418</span>            <span class="keyword">const</span> dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00419" name="l00419"></a><span class="lineno">  419</span>            <a class="code hl_function" href="namespacenz_1_1krnl.html#a04246c5218530f789a0ed4811b7ef3f3">LeakyReLU</a>(grid, block, output-&gt;data(), inputs[0]-&gt;output-&gt;data(), output-&gt;size(), alpha);</div>
<div class="line"><a id="l00420" name="l00420"></a><span class="lineno">  420</span>        }</div>
</div>
<div class="line"><a id="l00421" name="l00421"></a><span class="lineno">  421</span> </div>
<div class="foldopen" id="foldopen00422" data-start="{" data-end="}">
<div class="line"><a id="l00422" name="l00422"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_leaky_re_l_u_node.html#aa3fe1c9e74733c8c5e15e71ec14143f0">  422</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_leaky_re_l_u_node.html#aa3fe1c9e74733c8c5e15e71ec14143f0">LeakyReLUNode::backward</a>() {</div>
<div class="line"><a id="l00423" name="l00423"></a><span class="lineno">  423</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00424" name="l00424"></a><span class="lineno">  424</span>                <span class="keyword">const</span> dim3 block(256);</div>
<div class="line"><a id="l00425" name="l00425"></a><span class="lineno">  425</span>                <span class="keyword">const</span> dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00426" name="l00426"></a><span class="lineno">  426</span>                <a class="code hl_function" href="namespacenz_1_1krnl.html#a7eade95ddcf48141d69bb19803b22d51">LeakyReLUBackward</a>(grid, block, inputs[0]-&gt;output-&gt;grad(), inputs[0]-&gt;output-&gt;data(), output-&gt;grad(),</div>
<div class="line"><a id="l00427" name="l00427"></a><span class="lineno">  427</span>                                  output-&gt;size(), alpha);</div>
<div class="line"><a id="l00428" name="l00428"></a><span class="lineno">  428</span>            }</div>
<div class="line"><a id="l00429" name="l00429"></a><span class="lineno">  429</span>        }</div>
</div>
<div class="line"><a id="l00430" name="l00430"></a><span class="lineno">  430</span> </div>
<div class="foldopen" id="foldopen00431" data-start="{" data-end="}">
<div class="line"><a id="l00431" name="l00431"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_swish_node.html#a20f471bd6a03cf6d72d4e37eaba9fbb7">  431</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_swish_node.html#a20f471bd6a03cf6d72d4e37eaba9fbb7">SwishNode::SwishNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input) {</div>
<div class="line"><a id="l00432" name="l00432"></a><span class="lineno">  432</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00433" name="l00433"></a><span class="lineno">  433</span>            <span class="keywordtype">bool</span> requires_grad = input-&gt;output-&gt;requiresGrad();</div>
<div class="line"><a id="l00434" name="l00434"></a><span class="lineno">  434</span>            output = std::make_shared&lt;Tensor&gt;(input-&gt;output-&gt;shape(), requires_grad);</div>
<div class="line"><a id="l00435" name="l00435"></a><span class="lineno">  435</span>            type = <span class="stringliteral">&quot;Swish&quot;</span>;</div>
<div class="line"><a id="l00436" name="l00436"></a><span class="lineno">  436</span>        }</div>
</div>
<div class="line"><a id="l00437" name="l00437"></a><span class="lineno">  437</span> </div>
<div class="foldopen" id="foldopen00438" data-start="{" data-end="}">
<div class="line"><a id="l00438" name="l00438"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_swish_node.html#ab0fbf5a4d05c0df96b8aaffab36d92db">  438</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_swish_node.html#ab0fbf5a4d05c0df96b8aaffab36d92db">SwishNode::forward</a>() {</div>
<div class="line"><a id="l00439" name="l00439"></a><span class="lineno">  439</span>            <span class="keyword">const</span> dim3 block(256);</div>
<div class="line"><a id="l00440" name="l00440"></a><span class="lineno">  440</span>            <span class="keyword">const</span> dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00441" name="l00441"></a><span class="lineno">  441</span>            <a class="code hl_function" href="namespacenz_1_1krnl.html#a997aa5460fd64fadf9b701fbf73e3fb2">Swish</a>(grid, block, output-&gt;data(), inputs[0]-&gt;output-&gt;data(), output-&gt;size());</div>
<div class="line"><a id="l00442" name="l00442"></a><span class="lineno">  442</span>        }</div>
</div>
<div class="line"><a id="l00443" name="l00443"></a><span class="lineno">  443</span> </div>
<div class="foldopen" id="foldopen00444" data-start="{" data-end="}">
<div class="line"><a id="l00444" name="l00444"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_swish_node.html#a55758c143f9a941d24abc58a43ae5e9d">  444</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_swish_node.html#a55758c143f9a941d24abc58a43ae5e9d">SwishNode::backward</a>() {</div>
<div class="line"><a id="l00445" name="l00445"></a><span class="lineno">  445</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00446" name="l00446"></a><span class="lineno">  446</span>                <span class="keyword">const</span> dim3 block(256);</div>
<div class="line"><a id="l00447" name="l00447"></a><span class="lineno">  447</span>                <span class="keyword">const</span> dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00448" name="l00448"></a><span class="lineno">  448</span>                <a class="code hl_function" href="namespacenz_1_1krnl.html#a6c5a4b54442aab42df5afe8688e71596">SwishBackward</a>(grid, block, inputs[0]-&gt;output-&gt;grad(), inputs[0]-&gt;output-&gt;data(), output-&gt;data(),</div>
<div class="line"><a id="l00449" name="l00449"></a><span class="lineno">  449</span>                              output-&gt;grad(), output-&gt;size());</div>
<div class="line"><a id="l00450" name="l00450"></a><span class="lineno">  450</span>            }</div>
<div class="line"><a id="l00451" name="l00451"></a><span class="lineno">  451</span>        }</div>
</div>
<div class="line"><a id="l00452" name="l00452"></a><span class="lineno">  452</span> </div>
<div class="foldopen" id="foldopen00453" data-start="{" data-end="}">
<div class="line"><a id="l00453" name="l00453"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_e_l_u_node.html#a1dc0572d0688d2e65912b367286ac6ad">  453</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_e_l_u_node.html#a1dc0572d0688d2e65912b367286ac6ad">ELUNode::ELUNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input, Tensor::value_type alpha) {</div>
<div class="line"><a id="l00454" name="l00454"></a><span class="lineno">  454</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00455" name="l00455"></a><span class="lineno">  455</span>            <span class="keywordtype">bool</span> requires_grad = input-&gt;output-&gt;requiresGrad();</div>
<div class="line"><a id="l00456" name="l00456"></a><span class="lineno">  456</span>            output = std::make_shared&lt;Tensor&gt;(input-&gt;output-&gt;shape(), requires_grad);</div>
<div class="line"><a id="l00457" name="l00457"></a><span class="lineno">  457</span>            this-&gt;alpha = alpha;</div>
<div class="line"><a id="l00458" name="l00458"></a><span class="lineno">  458</span>            type = <span class="stringliteral">&quot;ELU&quot;</span>;</div>
<div class="line"><a id="l00459" name="l00459"></a><span class="lineno">  459</span>        }</div>
</div>
<div class="line"><a id="l00460" name="l00460"></a><span class="lineno">  460</span> </div>
<div class="foldopen" id="foldopen00461" data-start="{" data-end="}">
<div class="line"><a id="l00461" name="l00461"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_e_l_u_node.html#acd8b07d5fbd5a920d58d55a72a9ff092">  461</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_e_l_u_node.html#acd8b07d5fbd5a920d58d55a72a9ff092">ELUNode::forward</a>() {</div>
<div class="line"><a id="l00462" name="l00462"></a><span class="lineno">  462</span>            dim3 block(256);</div>
<div class="line"><a id="l00463" name="l00463"></a><span class="lineno">  463</span>            dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00464" name="l00464"></a><span class="lineno">  464</span>            <a class="code hl_function" href="namespacenz_1_1krnl.html#a0e82aca250b46ac8ded8cae8936d7e38">ExponentialLinearUnit</a>(grid, block, output-&gt;data(), inputs[0]-&gt;output-&gt;data(), output-&gt;size(), alpha);</div>
<div class="line"><a id="l00465" name="l00465"></a><span class="lineno">  465</span>        }</div>
</div>
<div class="line"><a id="l00466" name="l00466"></a><span class="lineno">  466</span> </div>
<div class="foldopen" id="foldopen00467" data-start="{" data-end="}">
<div class="line"><a id="l00467" name="l00467"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_e_l_u_node.html#a167cd647d2a6e3f273c250f15562a317">  467</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_e_l_u_node.html#a167cd647d2a6e3f273c250f15562a317">ELUNode::backward</a>() {</div>
<div class="line"><a id="l00468" name="l00468"></a><span class="lineno">  468</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00469" name="l00469"></a><span class="lineno">  469</span>                dim3 block(256);</div>
<div class="line"><a id="l00470" name="l00470"></a><span class="lineno">  470</span>                dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00471" name="l00471"></a><span class="lineno">  471</span>                <a class="code hl_function" href="namespacenz_1_1krnl.html#aee8ca471aa260bd1fca5b1797e229f9f">ELUBackward</a>(grid, block, inputs[0]-&gt;output-&gt;grad(), inputs[0]-&gt;output-&gt;data(), output-&gt;grad(),</div>
<div class="line"><a id="l00472" name="l00472"></a><span class="lineno">  472</span>                            output-&gt;size(), alpha);</div>
<div class="line"><a id="l00473" name="l00473"></a><span class="lineno">  473</span>            }</div>
<div class="line"><a id="l00474" name="l00474"></a><span class="lineno">  474</span>        }</div>
</div>
<div class="line"><a id="l00475" name="l00475"></a><span class="lineno">  475</span> </div>
<div class="foldopen" id="foldopen00476" data-start="{" data-end="}">
<div class="line"><a id="l00476" name="l00476"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_hard_sigmoid_node.html#a9fad60d7a07f6296aa5ce13acd6511d2">  476</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_hard_sigmoid_node.html#a9fad60d7a07f6296aa5ce13acd6511d2">HardSigmoidNode::HardSigmoidNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input, Tensor::value_type alpha, Tensor::value_type beta) {</div>
<div class="line"><a id="l00477" name="l00477"></a><span class="lineno">  477</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00478" name="l00478"></a><span class="lineno">  478</span>            <span class="keywordtype">bool</span> requires_grad = input-&gt;output-&gt;requiresGrad();</div>
<div class="line"><a id="l00479" name="l00479"></a><span class="lineno">  479</span>            output = std::make_shared&lt;Tensor&gt;(input-&gt;output-&gt;shape(), requires_grad);</div>
<div class="line"><a id="l00480" name="l00480"></a><span class="lineno">  480</span>            this-&gt;alpha = alpha;</div>
<div class="line"><a id="l00481" name="l00481"></a><span class="lineno">  481</span>            this-&gt;beta = beta;</div>
<div class="line"><a id="l00482" name="l00482"></a><span class="lineno">  482</span>            type = <span class="stringliteral">&quot;HardSigmoid&quot;</span>;</div>
<div class="line"><a id="l00483" name="l00483"></a><span class="lineno">  483</span>        }</div>
</div>
<div class="line"><a id="l00484" name="l00484"></a><span class="lineno">  484</span> </div>
<div class="foldopen" id="foldopen00485" data-start="{" data-end="}">
<div class="line"><a id="l00485" name="l00485"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_hard_sigmoid_node.html#a97590995aa192807d96a856ee2bcd71f">  485</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_hard_sigmoid_node.html#a97590995aa192807d96a856ee2bcd71f">HardSigmoidNode::forward</a>() {</div>
<div class="line"><a id="l00486" name="l00486"></a><span class="lineno">  486</span>            dim3 block(256);</div>
<div class="line"><a id="l00487" name="l00487"></a><span class="lineno">  487</span>            dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00488" name="l00488"></a><span class="lineno">  488</span>            <a class="code hl_function" href="namespacenz_1_1krnl.html#a52e449285e560185378234aecaf2f87c">HardSigmoid</a>(grid, block, output-&gt;data(), inputs[0]-&gt;output-&gt;data(), output-&gt;size(), alpha, beta);</div>
<div class="line"><a id="l00489" name="l00489"></a><span class="lineno">  489</span>        }</div>
</div>
<div class="line"><a id="l00490" name="l00490"></a><span class="lineno">  490</span> </div>
<div class="foldopen" id="foldopen00491" data-start="{" data-end="}">
<div class="line"><a id="l00491" name="l00491"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_hard_sigmoid_node.html#ad977c6a8c49252de4038f8ac08beed3c">  491</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_hard_sigmoid_node.html#ad977c6a8c49252de4038f8ac08beed3c">HardSigmoidNode::backward</a>() {</div>
<div class="line"><a id="l00492" name="l00492"></a><span class="lineno">  492</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00493" name="l00493"></a><span class="lineno">  493</span>                dim3 block(256);</div>
<div class="line"><a id="l00494" name="l00494"></a><span class="lineno">  494</span>                dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00495" name="l00495"></a><span class="lineno">  495</span>                <a class="code hl_function" href="namespacenz_1_1krnl.html#a43232f9472ad3b974351e59386208efa">HardSigmoidBackward</a>(grid, block, inputs[0]-&gt;output-&gt;grad(), inputs[0]-&gt;output-&gt;data(), output-&gt;grad(),</div>
<div class="line"><a id="l00496" name="l00496"></a><span class="lineno">  496</span>                                    output-&gt;size(), alpha, beta);</div>
<div class="line"><a id="l00497" name="l00497"></a><span class="lineno">  497</span>            }</div>
<div class="line"><a id="l00498" name="l00498"></a><span class="lineno">  498</span>        }</div>
</div>
<div class="line"><a id="l00499" name="l00499"></a><span class="lineno">  499</span> </div>
<div class="foldopen" id="foldopen00500" data-start="{" data-end="}">
<div class="line"><a id="l00500" name="l00500"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_hard_swish_node.html#a014a289c458494bf3bf02f266c129205">  500</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_hard_swish_node.html#a014a289c458494bf3bf02f266c129205">HardSwishNode::HardSwishNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input, Tensor::value_type alpha, Tensor::value_type beta) {</div>
<div class="line"><a id="l00501" name="l00501"></a><span class="lineno">  501</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00502" name="l00502"></a><span class="lineno">  502</span>            <span class="keywordtype">bool</span> requires_grad = input-&gt;output-&gt;requiresGrad();</div>
<div class="line"><a id="l00503" name="l00503"></a><span class="lineno">  503</span>            output = std::make_shared&lt;Tensor&gt;(input-&gt;output-&gt;shape(), requires_grad);</div>
<div class="line"><a id="l00504" name="l00504"></a><span class="lineno">  504</span>            this-&gt;alpha = alpha;</div>
<div class="line"><a id="l00505" name="l00505"></a><span class="lineno">  505</span>            this-&gt;beta = beta;</div>
<div class="line"><a id="l00506" name="l00506"></a><span class="lineno">  506</span>            type = <span class="stringliteral">&quot;HardSwish&quot;</span>;</div>
<div class="line"><a id="l00507" name="l00507"></a><span class="lineno">  507</span>        }</div>
</div>
<div class="line"><a id="l00508" name="l00508"></a><span class="lineno">  508</span> </div>
<div class="foldopen" id="foldopen00509" data-start="{" data-end="}">
<div class="line"><a id="l00509" name="l00509"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_hard_swish_node.html#afd48643b49c76a9c38b0bfc0c4a502f1">  509</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_hard_swish_node.html#afd48643b49c76a9c38b0bfc0c4a502f1">HardSwishNode::forward</a>() {</div>
<div class="line"><a id="l00510" name="l00510"></a><span class="lineno">  510</span>            dim3 block(256);</div>
<div class="line"><a id="l00511" name="l00511"></a><span class="lineno">  511</span>            dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00512" name="l00512"></a><span class="lineno">  512</span>            <a class="code hl_function" href="namespacenz_1_1krnl.html#aef9c028ed356b5684e103639bb23bcf0">HardSwish</a>(grid, block, output-&gt;data(), inputs[0]-&gt;output-&gt;data(), output-&gt;size(), alpha, beta);</div>
<div class="line"><a id="l00513" name="l00513"></a><span class="lineno">  513</span>        }</div>
</div>
<div class="line"><a id="l00514" name="l00514"></a><span class="lineno">  514</span> </div>
<div class="foldopen" id="foldopen00515" data-start="{" data-end="}">
<div class="line"><a id="l00515" name="l00515"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_hard_swish_node.html#ace2cc92220449c5425a75d8e7f35ff42">  515</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_hard_swish_node.html#ace2cc92220449c5425a75d8e7f35ff42">HardSwishNode::backward</a>() {</div>
<div class="line"><a id="l00516" name="l00516"></a><span class="lineno">  516</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00517" name="l00517"></a><span class="lineno">  517</span>                dim3 block(256);</div>
<div class="line"><a id="l00518" name="l00518"></a><span class="lineno">  518</span>                dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00519" name="l00519"></a><span class="lineno">  519</span>                <a class="code hl_function" href="namespacenz_1_1krnl.html#a455365870d43ff26687a731d15c4cdff">HardSwishBackward</a>(grid, block, inputs[0]-&gt;output-&gt;grad(), inputs[0]-&gt;output-&gt;data(), output-&gt;grad(),</div>
<div class="line"><a id="l00520" name="l00520"></a><span class="lineno">  520</span>                                  output-&gt;size(), alpha, beta);</div>
<div class="line"><a id="l00521" name="l00521"></a><span class="lineno">  521</span>            }</div>
<div class="line"><a id="l00522" name="l00522"></a><span class="lineno">  522</span>        }</div>
</div>
<div class="line"><a id="l00523" name="l00523"></a><span class="lineno">  523</span> </div>
<div class="foldopen" id="foldopen00524" data-start="{" data-end="}">
<div class="line"><a id="l00524" name="l00524"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_softmax_node.html#a6bd70cb3436435bac2055e86dfdb078b">  524</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_softmax_node.html#a6bd70cb3436435bac2055e86dfdb078b">SoftmaxNode::SoftmaxNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input) {</div>
<div class="line"><a id="l00525" name="l00525"></a><span class="lineno">  525</span>            <span class="keywordflow">if</span> (std::min(input-&gt;output-&gt;shape().H(), input-&gt;output-&gt;shape().W()) != 1) {</div>
<div class="line"><a id="l00526" name="l00526"></a><span class="lineno">  526</span>                <span class="keywordflow">throw</span> std::invalid_argument(<span class="stringliteral">&quot;SoftmaxNode: input must be 1D&quot;</span>);</div>
<div class="line"><a id="l00527" name="l00527"></a><span class="lineno">  527</span>            }</div>
<div class="line"><a id="l00528" name="l00528"></a><span class="lineno">  528</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00529" name="l00529"></a><span class="lineno">  529</span>            <span class="keywordtype">bool</span> requires_grad = input-&gt;output-&gt;requiresGrad();</div>
<div class="line"><a id="l00530" name="l00530"></a><span class="lineno">  530</span>            output = std::make_shared&lt;Tensor&gt;(input-&gt;output-&gt;shape(), requires_grad);</div>
<div class="line"><a id="l00531" name="l00531"></a><span class="lineno">  531</span>            type = <span class="stringliteral">&quot;Softmax&quot;</span>;</div>
<div class="line"><a id="l00532" name="l00532"></a><span class="lineno">  532</span>        }</div>
</div>
<div class="line"><a id="l00533" name="l00533"></a><span class="lineno">  533</span> </div>
<div class="foldopen" id="foldopen00534" data-start="{" data-end="}">
<div class="line"><a id="l00534" name="l00534"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_softmax_node.html#a93f7d936ff487db8e7dceb6ee0cdc38e">  534</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_softmax_node.html#a93f7d936ff487db8e7dceb6ee0cdc38e">SoftmaxNode::forward</a>() {</div>
<div class="line"><a id="l00535" name="l00535"></a><span class="lineno">  535</span>            <a class="code hl_function" href="namespacenz_1_1krnl.html#adbafc409d57fa0a9d78ecac5bf7b10a3">Softmax</a>(*output, *inputs[0]-&gt;output);</div>
<div class="line"><a id="l00536" name="l00536"></a><span class="lineno">  536</span>        }</div>
</div>
<div class="line"><a id="l00537" name="l00537"></a><span class="lineno">  537</span> </div>
<div class="foldopen" id="foldopen00538" data-start="{" data-end="}">
<div class="line"><a id="l00538" name="l00538"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_softmax_node.html#aa991e3bde7a3a5edbee62fab1cabba23">  538</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_softmax_node.html#aa991e3bde7a3a5edbee62fab1cabba23">SoftmaxNode::backward</a>() {</div>
<div class="line"><a id="l00539" name="l00539"></a><span class="lineno">  539</span>            <span class="keyword">auto</span> jacobian = softmaxJacobian(*output);</div>
<div class="line"><a id="l00540" name="l00540"></a><span class="lineno">  540</span>            <span class="keywordflow">if</span> (output-&gt;shape()[2] &gt; output-&gt;shape()[3]) {</div>
<div class="line"><a id="l00541" name="l00541"></a><span class="lineno">  541</span>                TensorCoreGEMMParallel(jacobian.data(), output-&gt;grad(), inputs[0]-&gt;output-&gt;grad(), jacobian.shape(),</div>
<div class="line"><a id="l00542" name="l00542"></a><span class="lineno">  542</span>                                       output-&gt;shape(), inputs[0]-&gt;output-&gt;shape());</div>
<div class="line"><a id="l00543" name="l00543"></a><span class="lineno">  543</span>            }</div>
<div class="line"><a id="l00544" name="l00544"></a><span class="lineno">  544</span>            <span class="keywordflow">else</span> {</div>
<div class="line"><a id="l00545" name="l00545"></a><span class="lineno">  545</span>                TensorCoreGEMMParallel(output-&gt;grad(), jacobian.data(), inputs[0]-&gt;output-&gt;grad(), output-&gt;shape(),</div>
<div class="line"><a id="l00546" name="l00546"></a><span class="lineno">  546</span>                                       jacobian.shape(), inputs[0]-&gt;output-&gt;shape());</div>
<div class="line"><a id="l00547" name="l00547"></a><span class="lineno">  547</span>            }</div>
<div class="line"><a id="l00548" name="l00548"></a><span class="lineno">  548</span>        }</div>
</div>
<div class="line"><a id="l00549" name="l00549"></a><span class="lineno">  549</span> </div>
<div class="foldopen" id="foldopen00550" data-start="{" data-end="}">
<div class="line"><a id="l00550" name="l00550"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_reshape_node.html#adb97b80637a53a9e1b9776f8fcae8ed7">  550</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_reshape_node.html#adb97b80637a53a9e1b9776f8fcae8ed7">ReshapeNode::ReshapeNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input, <span class="keyword">const</span> <a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">Tensor::shape_type</a>&amp; newShape) : newShape(newShape) {</div>
<div class="line"><a id="l00551" name="l00551"></a><span class="lineno">  551</span>            <span class="keywordflow">if</span> (input-&gt;output-&gt;shape().size() != newShape.<a class="code hl_function" href="classnz_1_1data_1_1_dimension.html#a073622bb031999163987ccf77f8edfb2">size</a>()) {</div>
<div class="line"><a id="l00552" name="l00552"></a><span class="lineno">  552</span>                throw std::invalid_argument(<span class="stringliteral">&quot;ReshapeNode: input and new shape must have the same number of dimensions&quot;</span>);</div>
<div class="line"><a id="l00553" name="l00553"></a><span class="lineno">  553</span>            }</div>
<div class="line"><a id="l00554" name="l00554"></a><span class="lineno">  554</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00555" name="l00555"></a><span class="lineno">  555</span>            output = std::make_shared&lt;Tensor&gt;(newShape, input-&gt;output-&gt;requiresGrad());</div>
<div class="line"><a id="l00556" name="l00556"></a><span class="lineno">  556</span>            type = <span class="stringliteral">&quot;Reshape&quot;</span>;</div>
<div class="line"><a id="l00557" name="l00557"></a><span class="lineno">  557</span>        }</div>
</div>
<div class="line"><a id="l00558" name="l00558"></a><span class="lineno">  558</span> </div>
<div class="foldopen" id="foldopen00559" data-start="{" data-end="}">
<div class="line"><a id="l00559" name="l00559"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_reshape_node.html#a8f9c9e9dbbe4db8d9420b9928ab369f1">  559</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_reshape_node.html#a8f9c9e9dbbe4db8d9420b9928ab369f1">ReshapeNode::forward</a>() {</div>
<div class="line"><a id="l00560" name="l00560"></a><span class="lineno">  560</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;float&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(output-&gt;data(), inputs[0]-&gt;output-&gt;data(),</div>
<div class="line"><a id="l00561" name="l00561"></a><span class="lineno">  561</span>                                                            output-&gt;size() * <span class="keyword">sizeof</span>(Tensor::value_type),</div>
<div class="line"><a id="l00562" name="l00562"></a><span class="lineno">  562</span>                                                            cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00563" name="l00563"></a><span class="lineno">  563</span>        }</div>
</div>
<div class="line"><a id="l00564" name="l00564"></a><span class="lineno">  564</span> </div>
<div class="foldopen" id="foldopen00565" data-start="{" data-end="}">
<div class="line"><a id="l00565" name="l00565"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_reshape_node.html#a28c90cc1c5dd3837dcbde0c1abc841d5">  565</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_reshape_node.html#a28c90cc1c5dd3837dcbde0c1abc841d5">ReshapeNode::backward</a>() {</div>
<div class="line"><a id="l00566" name="l00566"></a><span class="lineno">  566</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00567" name="l00567"></a><span class="lineno">  567</span>                <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;float&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(inputs[0]-&gt;output-&gt;grad(), output-&gt;grad(),</div>
<div class="line"><a id="l00568" name="l00568"></a><span class="lineno">  568</span>                                                                output-&gt;size() * <span class="keyword">sizeof</span>(Tensor::value_type),</div>
<div class="line"><a id="l00569" name="l00569"></a><span class="lineno">  569</span>                                                                cudaMemcpyDeviceToDevice);</div>
<div class="line"><a id="l00570" name="l00570"></a><span class="lineno">  570</span>            }</div>
<div class="line"><a id="l00571" name="l00571"></a><span class="lineno">  571</span>        }</div>
</div>
<div class="line"><a id="l00572" name="l00572"></a><span class="lineno">  572</span> </div>
<div class="foldopen" id="foldopen00573" data-start="{" data-end="}">
<div class="line"><a id="l00573" name="l00573"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_expand_node.html#a78874b46ee54a4a11f5d0ae2f79409c0">  573</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_expand_node.html#a78874b46ee54a4a11f5d0ae2f79409c0">ExpandNode::ExpandNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input, <span class="keyword">const</span> Tensor::size_type newBatch) : newBatch(newBatch) {</div>
<div class="line"><a id="l00574" name="l00574"></a><span class="lineno">  574</span>            <span class="keywordflow">if</span> (input-&gt;output-&gt;shape()[0] != 1) {</div>
<div class="line"><a id="l00575" name="l00575"></a><span class="lineno">  575</span>                throw std::invalid_argument(<span class="stringliteral">&quot;ExpandNode: input must have batch size 1&quot;</span>);</div>
<div class="line"><a id="l00576" name="l00576"></a><span class="lineno">  576</span>            }</div>
<div class="line"><a id="l00577" name="l00577"></a><span class="lineno">  577</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00578" name="l00578"></a><span class="lineno">  578</span>            output = std::make_shared&lt;Tensor&gt;(<a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">Tensor::shape_type</a>{</div>
<div class="line"><a id="l00579" name="l00579"></a><span class="lineno">  579</span>                                                  newBatch, input-&gt;output-&gt;shape()[1], input-&gt;output-&gt;shape()[2],</div>
<div class="line"><a id="l00580" name="l00580"></a><span class="lineno">  580</span>                                                  input-&gt;output-&gt;shape()[3]</div>
<div class="line"><a id="l00581" name="l00581"></a><span class="lineno">  581</span>                                              }, input-&gt;output-&gt;requiresGrad());</div>
<div class="line"><a id="l00582" name="l00582"></a><span class="lineno">  582</span>            type = <span class="stringliteral">&quot;Expand&quot;</span>;</div>
<div class="line"><a id="l00583" name="l00583"></a><span class="lineno">  583</span>        }</div>
</div>
<div class="line"><a id="l00584" name="l00584"></a><span class="lineno">  584</span> </div>
<div class="foldopen" id="foldopen00585" data-start="{" data-end="}">
<div class="line"><a id="l00585" name="l00585"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_expand_node.html#a883419ee48eca406c2deb6939aa1a546">  585</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_expand_node.html#a883419ee48eca406c2deb6939aa1a546">ExpandNode::forward</a>() {</div>
<div class="line"><a id="l00586" name="l00586"></a><span class="lineno">  586</span>            <span class="keyword">const</span> <span class="keyword">auto</span> size = inputs[0]-&gt;output-&gt;shape()[1] * inputs[0]-&gt;output-&gt;shape()[2] *</div>
<div class="line"><a id="l00587" name="l00587"></a><span class="lineno">  587</span>                inputs[0]-&gt;output-&gt;shape()[3];</div>
<div class="line"><a id="l00588" name="l00588"></a><span class="lineno">  588</span>            <span class="keyword">const</span> <span class="keyword">auto</span> total = size * newBatch;</div>
<div class="line"><a id="l00589" name="l00589"></a><span class="lineno">  589</span>            <span class="keyword">const</span> dim3 block(BLOCKSIZE);</div>
<div class="line"><a id="l00590" name="l00590"></a><span class="lineno">  590</span>            <span class="keyword">const</span> dim3 grid((total + block.x - 1) / block.x);</div>
<div class="line"><a id="l00591" name="l00591"></a><span class="lineno">  591</span>            <a class="code hl_function" href="namespacenz_1_1krnl.html#ae45dbebceb76ddf82fa5e6b9df882e62">Expand</a>(grid, block, output-&gt;data(), inputs[0]-&gt;output-&gt;data(), size, total);</div>
<div class="line"><a id="l00592" name="l00592"></a><span class="lineno">  592</span>        }</div>
</div>
<div class="line"><a id="l00593" name="l00593"></a><span class="lineno">  593</span> </div>
<div class="foldopen" id="foldopen00594" data-start="{" data-end="}">
<div class="line"><a id="l00594" name="l00594"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_expand_node.html#aac692ee5bf79df2148b9595030b46585">  594</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_expand_node.html#aac692ee5bf79df2148b9595030b46585">ExpandNode::backward</a>() {</div>
<div class="line"><a id="l00595" name="l00595"></a><span class="lineno">  595</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00596" name="l00596"></a><span class="lineno">  596</span>                <span class="keyword">const</span> <span class="keyword">auto</span> size = inputs[0]-&gt;output-&gt;shape()[1] * inputs[0]-&gt;output-&gt;shape()[2] *</div>
<div class="line"><a id="l00597" name="l00597"></a><span class="lineno">  597</span>                    inputs[0]-&gt;output-&gt;shape()[3];</div>
<div class="line"><a id="l00598" name="l00598"></a><span class="lineno">  598</span>                <span class="keyword">const</span> <span class="keyword">auto</span> total = size * newBatch;</div>
<div class="line"><a id="l00599" name="l00599"></a><span class="lineno">  599</span>                <span class="keyword">const</span> dim3 block(BLOCKSIZE);</div>
<div class="line"><a id="l00600" name="l00600"></a><span class="lineno">  600</span>                <span class="keyword">const</span> dim3 grid((total + block.x - 1) / block.x);</div>
<div class="line"><a id="l00601" name="l00601"></a><span class="lineno">  601</span>                <a class="code hl_function" href="namespacenz_1_1krnl.html#a454a28ef0e22014efca1ede4e954db65">Compress</a>(grid, block, inputs[0]-&gt;output-&gt;grad(), output-&gt;grad(), size, total);</div>
<div class="line"><a id="l00602" name="l00602"></a><span class="lineno">  602</span>            }</div>
<div class="line"><a id="l00603" name="l00603"></a><span class="lineno">  603</span>        }</div>
</div>
<div class="line"><a id="l00604" name="l00604"></a><span class="lineno">  604</span> </div>
<div class="foldopen" id="foldopen00605" data-start="{" data-end="}">
<div class="line"><a id="l00605" name="l00605"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_img2_col_node.html#a900bfad8e2c2706ffc9cf5cf20dee6dd">  605</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_img2_col_node.html#a900bfad8e2c2706ffc9cf5cf20dee6dd">Img2ColNode::Img2ColNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input, <span class="keyword">const</span> Tensor::size_type kernelHeight, <span class="keyword">const</span> Tensor::size_type kernelWidth,</div>
<div class="line"><a id="l00606" name="l00606"></a><span class="lineno">  606</span>                                 <span class="keyword">const</span> Tensor::size_type stride,</div>
<div class="line"><a id="l00607" name="l00607"></a><span class="lineno">  607</span>                                 <span class="keyword">const</span> Tensor::size_type padding) : kernelHeight(kernelHeight),</div>
<div class="line"><a id="l00608" name="l00608"></a><span class="lineno">  608</span>                                                                    kernelWidth(kernelWidth),</div>
<div class="line"><a id="l00609" name="l00609"></a><span class="lineno">  609</span>                                                                    stride(stride), padding(padding),</div>
<div class="line"><a id="l00610" name="l00610"></a><span class="lineno">  610</span>                                                                    outputHeight(</div>
<div class="line"><a id="l00611" name="l00611"></a><span class="lineno">  611</span>                                                                        (input-&gt;output-&gt;shape().H() + 2 * padding -</div>
<div class="line"><a id="l00612" name="l00612"></a><span class="lineno">  612</span>                                                                            kernelHeight) / stride + 1),</div>
<div class="line"><a id="l00613" name="l00613"></a><span class="lineno">  613</span>                                                                    outputWidth(</div>
<div class="line"><a id="l00614" name="l00614"></a><span class="lineno">  614</span>                                                                        (input-&gt;output-&gt;shape().W() + 2 * padding -</div>
<div class="line"><a id="l00615" name="l00615"></a><span class="lineno">  615</span>                                                                            kernelWidth) / stride + 1) {</div>
<div class="line"><a id="l00616" name="l00616"></a><span class="lineno">  616</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00617" name="l00617"></a><span class="lineno">  617</span>            output = std::make_shared&lt;Tensor&gt;(<a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">Tensor::shape_type</a>{</div>
<div class="line"><a id="l00618" name="l00618"></a><span class="lineno">  618</span>                                                  input-&gt;output-&gt;shape()[0], 1, outputHeight * outputWidth,</div>
<div class="line"><a id="l00619" name="l00619"></a><span class="lineno">  619</span>                                                  kernelHeight * kernelWidth * input-&gt;output-&gt;shape()[1]</div>
<div class="line"><a id="l00620" name="l00620"></a><span class="lineno">  620</span>                                              }, input-&gt;output-&gt;requiresGrad());</div>
<div class="line"><a id="l00621" name="l00621"></a><span class="lineno">  621</span>            type = <span class="stringliteral">&quot;Img2Col&quot;</span>;</div>
<div class="line"><a id="l00622" name="l00622"></a><span class="lineno">  622</span>        }</div>
</div>
<div class="line"><a id="l00623" name="l00623"></a><span class="lineno">  623</span> </div>
<div class="foldopen" id="foldopen00624" data-start="{" data-end="}">
<div class="line"><a id="l00624" name="l00624"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_img2_col_node.html#a199c6e1750035b8b9b4489de664b3ad3">  624</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_img2_col_node.html#a199c6e1750035b8b9b4489de664b3ad3">Img2ColNode::forward</a>() {</div>
<div class="line"><a id="l00625" name="l00625"></a><span class="lineno">  625</span>            iImg2col(output-&gt;data(), inputs[0]-&gt;output-&gt;data(), outputHeight, outputWidth,</div>
<div class="line"><a id="l00626" name="l00626"></a><span class="lineno">  626</span>                     inputs[0]-&gt;output-&gt;shape()[1],</div>
<div class="line"><a id="l00627" name="l00627"></a><span class="lineno">  627</span>                     kernelHeight, kernelWidth, stride, padding, inputs[0]-&gt;output-&gt;shape()[2],</div>
<div class="line"><a id="l00628" name="l00628"></a><span class="lineno">  628</span>                     inputs[0]-&gt;output-&gt;shape()[3],</div>
<div class="line"><a id="l00629" name="l00629"></a><span class="lineno">  629</span>                     inputs[0]-&gt;output-&gt;shape()[0]);</div>
<div class="line"><a id="l00630" name="l00630"></a><span class="lineno">  630</span>        }</div>
</div>
<div class="line"><a id="l00631" name="l00631"></a><span class="lineno">  631</span> </div>
<div class="foldopen" id="foldopen00632" data-start="{" data-end="}">
<div class="line"><a id="l00632" name="l00632"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_img2_col_node.html#a905c98a496d0106aed9af4e71205f653">  632</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_img2_col_node.html#a905c98a496d0106aed9af4e71205f653">Img2ColNode::backward</a>() {</div>
<div class="line"><a id="l00633" name="l00633"></a><span class="lineno">  633</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00634" name="l00634"></a><span class="lineno">  634</span>                iImg2colBackward(inputs[0]-&gt;output-&gt;grad(), output-&gt;grad(), outputHeight, outputWidth,</div>
<div class="line"><a id="l00635" name="l00635"></a><span class="lineno">  635</span>                                 inputs[0]-&gt;output-&gt;shape()[1],</div>
<div class="line"><a id="l00636" name="l00636"></a><span class="lineno">  636</span>                                 kernelHeight, kernelWidth, stride, padding, inputs[0]-&gt;output-&gt;shape()[2],</div>
<div class="line"><a id="l00637" name="l00637"></a><span class="lineno">  637</span>                                 inputs[0]-&gt;output-&gt;shape()[3],</div>
<div class="line"><a id="l00638" name="l00638"></a><span class="lineno">  638</span>                                 inputs[0]-&gt;output-&gt;shape()[0]);</div>
<div class="line"><a id="l00639" name="l00639"></a><span class="lineno">  639</span>            }</div>
<div class="line"><a id="l00640" name="l00640"></a><span class="lineno">  640</span>        }</div>
</div>
<div class="line"><a id="l00641" name="l00641"></a><span class="lineno">  641</span> </div>
<div class="foldopen" id="foldopen00642" data-start="{" data-end="}">
<div class="line"><a id="l00642" name="l00642"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_col2_img_node.html#a247f00c842e0420bac145f90c688a74a">  642</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_col2_img_node.html#a247f00c842e0420bac145f90c688a74a">Col2ImgNode::Col2ImgNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input, <span class="keyword">const</span> Tensor::size_type outputHeight,</div>
<div class="line"><a id="l00643" name="l00643"></a><span class="lineno">  643</span>                                 <span class="keyword">const</span> Tensor::size_type outputWidth) : outputHeight(outputHeight),</div>
<div class="line"><a id="l00644" name="l00644"></a><span class="lineno">  644</span>                                                                        outputWidth(outputWidth),</div>
<div class="line"><a id="l00645" name="l00645"></a><span class="lineno">  645</span>                                                                        outputChannels(input-&gt;output-&gt;shape()[3]) {</div>
<div class="line"><a id="l00646" name="l00646"></a><span class="lineno">  646</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00647" name="l00647"></a><span class="lineno">  647</span>            output = std::make_shared&lt;Tensor&gt;(<a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">Tensor::shape_type</a>(</div>
<div class="line"><a id="l00648" name="l00648"></a><span class="lineno">  648</span>                input-&gt;output-&gt;shape()[0],</div>
<div class="line"><a id="l00649" name="l00649"></a><span class="lineno">  649</span>                outputChannels,</div>
<div class="line"><a id="l00650" name="l00650"></a><span class="lineno">  650</span>                outputHeight,</div>
<div class="line"><a id="l00651" name="l00651"></a><span class="lineno">  651</span>                outputWidth), input-&gt;output-&gt;requiresGrad());</div>
<div class="line"><a id="l00652" name="l00652"></a><span class="lineno">  652</span>            type = <span class="stringliteral">&quot;Col2Img&quot;</span>;</div>
<div class="line"><a id="l00653" name="l00653"></a><span class="lineno">  653</span>        }</div>
</div>
<div class="line"><a id="l00654" name="l00654"></a><span class="lineno">  654</span> </div>
<div class="foldopen" id="foldopen00655" data-start="{" data-end="}">
<div class="line"><a id="l00655" name="l00655"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_col2_img_node.html#ae70df2f92889693ba699051af3de703f">  655</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_col2_img_node.html#ae70df2f92889693ba699051af3de703f">Col2ImgNode::forward</a>() {</div>
<div class="line"><a id="l00656" name="l00656"></a><span class="lineno">  656</span>            iCol2img(output-&gt;data(), inputs[0]-&gt;output-&gt;data(), outputHeight, outputWidth, outputChannels,</div>
<div class="line"><a id="l00657" name="l00657"></a><span class="lineno">  657</span>                inputs[0]-&gt;output-&gt;shape()[0]);</div>
<div class="line"><a id="l00658" name="l00658"></a><span class="lineno">  658</span>        }</div>
</div>
<div class="line"><a id="l00659" name="l00659"></a><span class="lineno">  659</span> </div>
<div class="foldopen" id="foldopen00660" data-start="{" data-end="}">
<div class="line"><a id="l00660" name="l00660"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_col2_img_node.html#a372c237486b96cadfbc71fe7e3a16bdd">  660</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_col2_img_node.html#a372c237486b96cadfbc71fe7e3a16bdd">Col2ImgNode::backward</a>() {</div>
<div class="line"><a id="l00661" name="l00661"></a><span class="lineno">  661</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00662" name="l00662"></a><span class="lineno">  662</span>                iCol2imgBackward(inputs[0]-&gt;output-&gt;grad(), output-&gt;grad(), outputHeight, outputWidth, outputChannels,</div>
<div class="line"><a id="l00663" name="l00663"></a><span class="lineno">  663</span>                inputs[0]-&gt;output-&gt;shape()[0]);</div>
<div class="line"><a id="l00664" name="l00664"></a><span class="lineno">  664</span>            }</div>
<div class="line"><a id="l00665" name="l00665"></a><span class="lineno">  665</span>        }</div>
</div>
<div class="line"><a id="l00666" name="l00666"></a><span class="lineno">  666</span> </div>
<div class="foldopen" id="foldopen00667" data-start="{" data-end="}">
<div class="line"><a id="l00667" name="l00667"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_average_pooling_node.html#add1055d87bda108863a4c3bf8dca5055">  667</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_average_pooling_node.html#add1055d87bda108863a4c3bf8dca5055">AveragePoolingNode::AveragePoolingNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input, Tensor::size_type poolSize, Tensor::size_type stride,</div>
<div class="line"><a id="l00668" name="l00668"></a><span class="lineno">  668</span>            Tensor::size_type padding) : poolSize(poolSize), stride(stride), padding(padding) {</div>
<div class="line"><a id="l00669" name="l00669"></a><span class="lineno">  669</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00670" name="l00670"></a><span class="lineno">  670</span>            output = std::make_shared&lt;Tensor&gt;(<a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">Tensor::shape_type</a>{</div>
<div class="line"><a id="l00671" name="l00671"></a><span class="lineno">  671</span>                input-&gt;output-&gt;shape()[0], input-&gt;output-&gt;shape()[1],</div>
<div class="line"><a id="l00672" name="l00672"></a><span class="lineno">  672</span>                OUTPUT_DIM(input-&gt;output-&gt;shape()[2], poolSize, stride, padding),</div>
<div class="line"><a id="l00673" name="l00673"></a><span class="lineno">  673</span>                OUTPUT_DIM(input-&gt;output-&gt;shape()[3], poolSize, stride, padding)</div>
<div class="line"><a id="l00674" name="l00674"></a><span class="lineno">  674</span>            }, input-&gt;output-&gt;requiresGrad());</div>
<div class="line"><a id="l00675" name="l00675"></a><span class="lineno">  675</span>            type = <span class="stringliteral">&quot;AveragePooling&quot;</span>;</div>
<div class="line"><a id="l00676" name="l00676"></a><span class="lineno">  676</span>        }</div>
</div>
<div class="line"><a id="l00677" name="l00677"></a><span class="lineno">  677</span> </div>
<div class="foldopen" id="foldopen00678" data-start="{" data-end="}">
<div class="line"><a id="l00678" name="l00678"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_average_pooling_node.html#ab206cec144b3b88af4a90c0eb0f5733c">  678</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_average_pooling_node.html#ab206cec144b3b88af4a90c0eb0f5733c">AveragePoolingNode::forward</a>() {</div>
<div class="line"><a id="l00679" name="l00679"></a><span class="lineno">  679</span>            iAveragePooling(output-&gt;data(), inputs[0]-&gt;output-&gt;data(), poolSize, stride, padding, inputs[0]-&gt;output-&gt;shape()[0],</div>
<div class="line"><a id="l00680" name="l00680"></a><span class="lineno">  680</span>                inputs[0]-&gt;output-&gt;shape()[1], inputs[0]-&gt;output-&gt;shape()[2], inputs[0]-&gt;output-&gt;shape()[3],</div>
<div class="line"><a id="l00681" name="l00681"></a><span class="lineno">  681</span>                output-&gt;shape()[2], output-&gt;shape()[3]);</div>
<div class="line"><a id="l00682" name="l00682"></a><span class="lineno">  682</span>        }</div>
</div>
<div class="line"><a id="l00683" name="l00683"></a><span class="lineno">  683</span> </div>
<div class="foldopen" id="foldopen00684" data-start="{" data-end="}">
<div class="line"><a id="l00684" name="l00684"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_average_pooling_node.html#a12dcec668190c3f466f5bd5e0b3f96a7">  684</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_average_pooling_node.html#a12dcec668190c3f466f5bd5e0b3f96a7">AveragePoolingNode::backward</a>() {</div>
<div class="line"><a id="l00685" name="l00685"></a><span class="lineno">  685</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00686" name="l00686"></a><span class="lineno">  686</span>                iAveragePoolingBackward(inputs[0]-&gt;output-&gt;grad(), output-&gt;grad(), poolSize, stride, padding, inputs[0]-&gt;output-&gt;shape()[0],</div>
<div class="line"><a id="l00687" name="l00687"></a><span class="lineno">  687</span>                inputs[0]-&gt;output-&gt;shape()[1], inputs[0]-&gt;output-&gt;shape()[2], inputs[0]-&gt;output-&gt;shape()[3],</div>
<div class="line"><a id="l00688" name="l00688"></a><span class="lineno">  688</span>                output-&gt;shape()[2], output-&gt;shape()[3]);</div>
<div class="line"><a id="l00689" name="l00689"></a><span class="lineno">  689</span>            }</div>
<div class="line"><a id="l00690" name="l00690"></a><span class="lineno">  690</span>        }</div>
</div>
<div class="line"><a id="l00691" name="l00691"></a><span class="lineno">  691</span> </div>
<div class="foldopen" id="foldopen00692" data-start="{" data-end="}">
<div class="line"><a id="l00692" name="l00692"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_global_avg_pool_node.html#ab1e87b4e3649f8a67bfc5a83bb3600e0">  692</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_global_avg_pool_node.html#ab1e87b4e3649f8a67bfc5a83bb3600e0">GlobalAvgPoolNode::GlobalAvgPoolNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input) {</div>
<div class="line"><a id="l00693" name="l00693"></a><span class="lineno">  693</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00694" name="l00694"></a><span class="lineno">  694</span>            output = std::make_shared&lt;Tensor&gt;(<a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">Tensor::shape_type</a>{</div>
<div class="line"><a id="l00695" name="l00695"></a><span class="lineno">  695</span>                input-&gt;output-&gt;shape()[0], input-&gt;output-&gt;shape()[1], 1, 1</div>
<div class="line"><a id="l00696" name="l00696"></a><span class="lineno">  696</span>            }, input-&gt;output-&gt;requiresGrad());</div>
<div class="line"><a id="l00697" name="l00697"></a><span class="lineno">  697</span>            type = <span class="stringliteral">&quot;GlobalAvgPool&quot;</span>;</div>
<div class="line"><a id="l00698" name="l00698"></a><span class="lineno">  698</span>        }</div>
</div>
<div class="line"><a id="l00699" name="l00699"></a><span class="lineno">  699</span> </div>
<div class="foldopen" id="foldopen00700" data-start="{" data-end="}">
<div class="line"><a id="l00700" name="l00700"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_global_avg_pool_node.html#ad8b2bb2ce47ab3c227f8c33f3a19fb91">  700</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_global_avg_pool_node.html#ad8b2bb2ce47ab3c227f8c33f3a19fb91">GlobalAvgPoolNode::forward</a>() {</div>
<div class="line"><a id="l00701" name="l00701"></a><span class="lineno">  701</span>            <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; inputs[0]-&gt;output-&gt;shape()[0]; i++) {</div>
<div class="line"><a id="l00702" name="l00702"></a><span class="lineno">  702</span>                <span class="keywordflow">for</span> (<span class="keyword">auto</span> j = 0; j &lt; inputs[0]-&gt;output-&gt;shape()[1]; j++) {</div>
<div class="line"><a id="l00703" name="l00703"></a><span class="lineno">  703</span>                    output-&gt;fillMatrix(inputs[0]-&gt;output-&gt;sum(i, j) / <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span><span class="keyword">&gt;</span>((inputs[0]-&gt;output-&gt;shape()[2] *</div>
<div class="line"><a id="l00704" name="l00704"></a><span class="lineno">  704</span>                        inputs[0]-&gt;output-&gt;shape()[3])), i, j);</div>
<div class="line"><a id="l00705" name="l00705"></a><span class="lineno">  705</span>                }</div>
<div class="line"><a id="l00706" name="l00706"></a><span class="lineno">  706</span>            }</div>
<div class="line"><a id="l00707" name="l00707"></a><span class="lineno">  707</span>        }</div>
</div>
<div class="line"><a id="l00708" name="l00708"></a><span class="lineno">  708</span> </div>
<div class="foldopen" id="foldopen00709" data-start="{" data-end="}">
<div class="line"><a id="l00709" name="l00709"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_global_avg_pool_node.html#a7a075619e9c976875118783bbdac8739">  709</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_global_avg_pool_node.html#a7a075619e9c976875118783bbdac8739">GlobalAvgPoolNode::backward</a>() {</div>
<div class="line"><a id="l00710" name="l00710"></a><span class="lineno">  710</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00711" name="l00711"></a><span class="lineno">  711</span>                iGlobalAvgPoolBackward(inputs[0]-&gt;output-&gt;grad(), output-&gt;grad(), inputs[0]-&gt;output-&gt;shape()[0],</div>
<div class="line"><a id="l00712" name="l00712"></a><span class="lineno">  712</span>                    inputs[0]-&gt;output-&gt;shape()[1], inputs[0]-&gt;output-&gt;shape()[2], inputs[0]-&gt;output-&gt;shape()[3]);</div>
<div class="line"><a id="l00713" name="l00713"></a><span class="lineno">  713</span>            }</div>
<div class="line"><a id="l00714" name="l00714"></a><span class="lineno">  714</span>        }</div>
</div>
<div class="line"><a id="l00715" name="l00715"></a><span class="lineno">  715</span> </div>
<div class="foldopen" id="foldopen00716" data-start="{" data-end="}">
<div class="line"><a id="l00716" name="l00716"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_max_pooling_node.html#a56018af1d41b8058deabe63c38e03c0d">  716</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_max_pooling_node.html#a56018af1d41b8058deabe63c38e03c0d">MaxPoolingNode::MaxPoolingNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input, Tensor::size_type poolSize, Tensor::size_type stride,</div>
<div class="line"><a id="l00717" name="l00717"></a><span class="lineno">  717</span>            Tensor::size_type padding) : poolSize(poolSize), stride(stride), padding(padding) {</div>
<div class="line"><a id="l00718" name="l00718"></a><span class="lineno">  718</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00719" name="l00719"></a><span class="lineno">  719</span>            output = std::make_shared&lt;Tensor&gt;(<a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">Tensor::shape_type</a>{</div>
<div class="line"><a id="l00720" name="l00720"></a><span class="lineno">  720</span>                input-&gt;output-&gt;shape()[0], input-&gt;output-&gt;shape()[1],</div>
<div class="line"><a id="l00721" name="l00721"></a><span class="lineno">  721</span>                OUTPUT_DIM(input-&gt;output-&gt;shape()[2], poolSize, stride, padding),</div>
<div class="line"><a id="l00722" name="l00722"></a><span class="lineno">  722</span>                OUTPUT_DIM(input-&gt;output-&gt;shape()[3], poolSize, stride, padding)</div>
<div class="line"><a id="l00723" name="l00723"></a><span class="lineno">  723</span>            }, input-&gt;output-&gt;requiresGrad());</div>
<div class="line"><a id="l00724" name="l00724"></a><span class="lineno">  724</span>            position = std::make_shared&lt;Tensor&gt;(<a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">Tensor::shape_type</a>{</div>
<div class="line"><a id="l00725" name="l00725"></a><span class="lineno">  725</span>                input-&gt;output-&gt;shape()[0], input-&gt;output-&gt;shape()[1],</div>
<div class="line"><a id="l00726" name="l00726"></a><span class="lineno">  726</span>                OUTPUT_DIM(input-&gt;output-&gt;shape()[2], poolSize, stride, padding),</div>
<div class="line"><a id="l00727" name="l00727"></a><span class="lineno">  727</span>                OUTPUT_DIM(input-&gt;output-&gt;shape()[3], poolSize, stride, padding)</div>
<div class="line"><a id="l00728" name="l00728"></a><span class="lineno">  728</span>            }, <span class="keyword">false</span>);</div>
<div class="line"><a id="l00729" name="l00729"></a><span class="lineno">  729</span>        }</div>
</div>
<div class="line"><a id="l00730" name="l00730"></a><span class="lineno">  730</span> </div>
<div class="foldopen" id="foldopen00731" data-start="{" data-end="}">
<div class="line"><a id="l00731" name="l00731"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_max_pooling_node.html#a2dd8c57551f76d957ef97121c6df2adc">  731</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_max_pooling_node.html#a2dd8c57551f76d957ef97121c6df2adc">MaxPoolingNode::forward</a>() {</div>
<div class="line"><a id="l00732" name="l00732"></a><span class="lineno">  732</span>            iMaxPooling(output-&gt;data(), position-&gt;data(), inputs[0]-&gt;output-&gt;data(), poolSize, stride, padding,</div>
<div class="line"><a id="l00733" name="l00733"></a><span class="lineno">  733</span>                inputs[0]-&gt;output-&gt;shape()[0], inputs[0]-&gt;output-&gt;shape()[1], inputs[0]-&gt;output-&gt;shape()[2],</div>
<div class="line"><a id="l00734" name="l00734"></a><span class="lineno">  734</span>                inputs[0]-&gt;output-&gt;shape()[3], output-&gt;shape()[2], output-&gt;shape()[3]);</div>
<div class="line"><a id="l00735" name="l00735"></a><span class="lineno">  735</span>        }</div>
</div>
<div class="line"><a id="l00736" name="l00736"></a><span class="lineno">  736</span> </div>
<div class="foldopen" id="foldopen00737" data-start="{" data-end="}">
<div class="line"><a id="l00737" name="l00737"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_max_pooling_node.html#ae0a0a0b7303101c4417564babf0fcd5f">  737</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_max_pooling_node.html#ae0a0a0b7303101c4417564babf0fcd5f">MaxPoolingNode::backward</a>() {</div>
<div class="line"><a id="l00738" name="l00738"></a><span class="lineno">  738</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00739" name="l00739"></a><span class="lineno">  739</span>                iMaxPoolingBackward(inputs[0]-&gt;output-&gt;grad(), position-&gt;data(), output-&gt;grad(), poolSize, stride, padding,</div>
<div class="line"><a id="l00740" name="l00740"></a><span class="lineno">  740</span>                inputs[0]-&gt;output-&gt;shape()[0], inputs[0]-&gt;output-&gt;shape()[1], inputs[0]-&gt;output-&gt;shape()[2],</div>
<div class="line"><a id="l00741" name="l00741"></a><span class="lineno">  741</span>                inputs[0]-&gt;output-&gt;shape()[3], output-&gt;shape()[2], output-&gt;shape()[3]);</div>
<div class="line"><a id="l00742" name="l00742"></a><span class="lineno">  742</span>            }</div>
<div class="line"><a id="l00743" name="l00743"></a><span class="lineno">  743</span>        }</div>
</div>
<div class="line"><a id="l00744" name="l00744"></a><span class="lineno">  744</span> </div>
<div class="foldopen" id="foldopen00745" data-start="{" data-end="}">
<div class="line"><a id="l00745" name="l00745"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_global_max_pool_node.html#a64613a073db56dbc1526aeb226497a73">  745</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_global_max_pool_node.html#a64613a073db56dbc1526aeb226497a73">GlobalMaxPoolNode::GlobalMaxPoolNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input) {</div>
<div class="line"><a id="l00746" name="l00746"></a><span class="lineno">  746</span>            inputs.push_back(input);</div>
<div class="line"><a id="l00747" name="l00747"></a><span class="lineno">  747</span>            output = std::make_shared&lt;Tensor&gt;(<a class="code hl_class" href="classnz_1_1data_1_1_dimension.html">Tensor::shape_type</a>{</div>
<div class="line"><a id="l00748" name="l00748"></a><span class="lineno">  748</span>                input-&gt;output-&gt;shape()[0], input-&gt;output-&gt;shape()[1], 1, 1</div>
<div class="line"><a id="l00749" name="l00749"></a><span class="lineno">  749</span>            }, input-&gt;output-&gt;requiresGrad());</div>
<div class="line"><a id="l00750" name="l00750"></a><span class="lineno">  750</span>            type = <span class="stringliteral">&quot;GlobalMaxPool&quot;</span>;</div>
<div class="line"><a id="l00751" name="l00751"></a><span class="lineno">  751</span>        }</div>
</div>
<div class="line"><a id="l00752" name="l00752"></a><span class="lineno">  752</span> </div>
<div class="foldopen" id="foldopen00753" data-start="{" data-end="}">
<div class="line"><a id="l00753" name="l00753"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_global_max_pool_node.html#ae35d13815fe6cb9bb46abe235e9469aa">  753</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_global_max_pool_node.html#ae35d13815fe6cb9bb46abe235e9469aa">GlobalMaxPoolNode::forward</a>() {</div>
<div class="line"><a id="l00754" name="l00754"></a><span class="lineno">  754</span>            <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; inputs[0]-&gt;output-&gt;shape()[0]; i++) {</div>
<div class="line"><a id="l00755" name="l00755"></a><span class="lineno">  755</span>                <span class="keywordflow">for</span> (<span class="keyword">auto</span> j = 0; j &lt; inputs[0]-&gt;output-&gt;shape()[1]; j++) {</div>
<div class="line"><a id="l00756" name="l00756"></a><span class="lineno">  756</span>                    output-&gt;fillMatrix(inputs[0]-&gt;output-&gt;max(i, j), i, j);</div>
<div class="line"><a id="l00757" name="l00757"></a><span class="lineno">  757</span>                }</div>
<div class="line"><a id="l00758" name="l00758"></a><span class="lineno">  758</span>            }</div>
<div class="line"><a id="l00759" name="l00759"></a><span class="lineno">  759</span>        }</div>
</div>
<div class="line"><a id="l00760" name="l00760"></a><span class="lineno">  760</span> </div>
<div class="foldopen" id="foldopen00761" data-start="{" data-end="}">
<div class="line"><a id="l00761" name="l00761"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1calc_1_1_global_max_pool_node.html#a2242a5196a07fdedc0a66ef457f57487">  761</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1calc_1_1_global_max_pool_node.html#a2242a5196a07fdedc0a66ef457f57487">GlobalMaxPoolNode::backward</a>() {</div>
<div class="line"><a id="l00762" name="l00762"></a><span class="lineno">  762</span>            <span class="keywordflow">if</span> (inputs[0]-&gt;output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00763" name="l00763"></a><span class="lineno">  763</span>                <span class="keyword">const</span> <span class="keyword">auto</span> data = output-&gt;hostData();</div>
<div class="line"><a id="l00764" name="l00764"></a><span class="lineno">  764</span>                <span class="keyword">const</span> <span class="keyword">auto</span> grad = output-&gt;hostGrad();</div>
<div class="line"><a id="l00765" name="l00765"></a><span class="lineno">  765</span>                <span class="keywordflow">for</span> (<span class="keyword">auto</span> i = 0; i &lt; inputs[0]-&gt;output-&gt;shape()[0]; i++) {</div>
<div class="line"><a id="l00766" name="l00766"></a><span class="lineno">  766</span>                    <span class="keywordflow">for</span> (<span class="keyword">auto</span> j = 0; j &lt; inputs[0]-&gt;output-&gt;shape()[1]; j++) {</div>
<div class="line"><a id="l00767" name="l00767"></a><span class="lineno">  767</span>                        <span class="keyword">auto</span> idx = i * inputs[0]-&gt;output-&gt;shape()[1] + j;</div>
<div class="line"><a id="l00768" name="l00768"></a><span class="lineno">  768</span>                        inputs[0]-&gt;output-&gt;setData(inputs[0]-&gt;output-&gt;find(data[idx], i, j), grad[idx], <span class="keyword">true</span>);</div>
<div class="line"><a id="l00769" name="l00769"></a><span class="lineno">  769</span>                    }</div>
<div class="line"><a id="l00770" name="l00770"></a><span class="lineno">  770</span>                }</div>
<div class="line"><a id="l00771" name="l00771"></a><span class="lineno">  771</span>            }</div>
<div class="line"><a id="l00772" name="l00772"></a><span class="lineno">  772</span>        }</div>
</div>
<div class="line"><a id="l00773" name="l00773"></a><span class="lineno">  773</span>    }</div>
<div class="line"><a id="l00774" name="l00774"></a><span class="lineno">  774</span> </div>
<div class="line"><a id="l00775" name="l00775"></a><span class="lineno">  775</span>    <span class="keyword">namespace </span>loss {</div>
<div class="foldopen" id="foldopen00776" data-start="{" data-end="}">
<div class="line"><a id="l00776" name="l00776"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1loss_1_1_mean_squared_error_node.html#ac9e098d63556329314c7389b779169b6">  776</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1loss_1_1_mean_squared_error_node.html#ac9e098d63556329314c7389b779169b6">MeanSquaredErrorNode::MeanSquaredErrorNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input1, <a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input2):</div>
<div class="line"><a id="l00777" name="l00777"></a><span class="lineno">  777</span>            OutputNode(input1) {</div>
<div class="line"><a id="l00778" name="l00778"></a><span class="lineno">  778</span>            <span class="keywordflow">if</span> (input1-&gt;output-&gt;shape() != input2-&gt;output-&gt;shape()) {</div>
<div class="line"><a id="l00779" name="l00779"></a><span class="lineno">  779</span>                throw std::invalid_argument(<span class="stringliteral">&quot;input1 and input2 should have the same shape&quot;</span>);</div>
<div class="line"><a id="l00780" name="l00780"></a><span class="lineno">  780</span>            }</div>
<div class="line"><a id="l00781" name="l00781"></a><span class="lineno">  781</span>            inputs.push_back(input2);</div>
<div class="line"><a id="l00782" name="l00782"></a><span class="lineno">  782</span>            type = <span class="stringliteral">&quot;MeanSquaredError&quot;</span>;</div>
<div class="line"><a id="l00783" name="l00783"></a><span class="lineno">  783</span>        }</div>
</div>
<div class="line"><a id="l00784" name="l00784"></a><span class="lineno">  784</span> </div>
<div class="foldopen" id="foldopen00785" data-start="{" data-end="}">
<div class="line"><a id="l00785" name="l00785"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1loss_1_1_mean_squared_error_node.html#ae81d6afb059f76617ea034032c12ec13">  785</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1loss_1_1_mean_squared_error_node.html#ae81d6afb059f76617ea034032c12ec13">MeanSquaredErrorNode::forward</a>() {</div>
<div class="line"><a id="l00786" name="l00786"></a><span class="lineno">  786</span>            OutputNode::forward();</div>
<div class="line"><a id="l00787" name="l00787"></a><span class="lineno">  787</span>            <span class="keyword">const</span> dim3 block(256);</div>
<div class="line"><a id="l00788" name="l00788"></a><span class="lineno">  788</span>            <span class="keyword">const</span> dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00789" name="l00789"></a><span class="lineno">  789</span>            <span class="keywordtype">float</span>* result;</div>
<div class="line"><a id="l00790" name="l00790"></a><span class="lineno">  790</span>            <span class="keyword">auto</span>* result_host = <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span>*<span class="keyword">&gt;</span>(malloc(grid.x * <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>)));</div>
<div class="line"><a id="l00791" name="l00791"></a><span class="lineno">  791</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;Tensor::value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;result, grid.x * <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>));</div>
<div class="line"><a id="l00792" name="l00792"></a><span class="lineno">  792</span>            <a class="code hl_function" href="namespacenz_1_1krnl.html#af76ce6a930db4def5ceb51350af72f3c">MeanSquaredError</a>(grid, block, block.x / WARP_SIZE * <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>), result, inputs[0]-&gt;output-&gt;data(),</div>
<div class="line"><a id="l00793" name="l00793"></a><span class="lineno">  793</span>                             inputs[1]-&gt;output-&gt;data(), output-&gt;size());</div>
<div class="line"><a id="l00794" name="l00794"></a><span class="lineno">  794</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;Tensor::value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(</div>
<div class="line"><a id="l00795" name="l00795"></a><span class="lineno">  795</span>                result_host, result, grid.x * <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>), cudaMemcpyDeviceToHost);</div>
<div class="line"><a id="l00796" name="l00796"></a><span class="lineno">  796</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;Tensor::value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">syncData</a>(result_host);</div>
<div class="line"><a id="l00797" name="l00797"></a><span class="lineno">  797</span>            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; grid.x; i++) {</div>
<div class="line"><a id="l00798" name="l00798"></a><span class="lineno">  798</span>                loss += result_host[i];</div>
<div class="line"><a id="l00799" name="l00799"></a><span class="lineno">  799</span>            }</div>
<div class="line"><a id="l00800" name="l00800"></a><span class="lineno">  800</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;Tensor::value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a785cf34395067f425e032d9bd5e1fa20">free</a>(result);</div>
<div class="line"><a id="l00801" name="l00801"></a><span class="lineno">  801</span>            free(result_host);</div>
<div class="line"><a id="l00802" name="l00802"></a><span class="lineno">  802</span>        }</div>
</div>
<div class="line"><a id="l00803" name="l00803"></a><span class="lineno">  803</span> </div>
<div class="foldopen" id="foldopen00804" data-start="{" data-end="}">
<div class="line"><a id="l00804" name="l00804"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1loss_1_1_mean_squared_error_node.html#a8ccbbad9b8bb2111d24af789020337ce">  804</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1loss_1_1_mean_squared_error_node.html#a8ccbbad9b8bb2111d24af789020337ce">MeanSquaredErrorNode::backward</a>() {</div>
<div class="line"><a id="l00805" name="l00805"></a><span class="lineno">  805</span>            <span class="keywordflow">if</span> (output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00806" name="l00806"></a><span class="lineno">  806</span>                <span class="keyword">const</span> dim3 block(256);</div>
<div class="line"><a id="l00807" name="l00807"></a><span class="lineno">  807</span>                <span class="keyword">const</span> dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00808" name="l00808"></a><span class="lineno">  808</span>                <a class="code hl_function" href="namespacenz_1_1krnl.html#ae77920db6adf79a17dbfb1dbf1ab5656">MSEBackward</a>(grid, block, output-&gt;grad(), inputs[0]-&gt;output-&gt;data(), inputs[1]-&gt;output-&gt;data(),</div>
<div class="line"><a id="l00809" name="l00809"></a><span class="lineno">  809</span>                            output-&gt;size());</div>
<div class="line"><a id="l00810" name="l00810"></a><span class="lineno">  810</span>            }</div>
<div class="line"><a id="l00811" name="l00811"></a><span class="lineno">  811</span>        }</div>
</div>
<div class="line"><a id="l00812" name="l00812"></a><span class="lineno">  812</span> </div>
<div class="foldopen" id="foldopen00813" data-start="{" data-end="}">
<div class="line"><a id="l00813" name="l00813"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1loss_1_1_binary_cross_entropy_node.html#a79a7fb04d377c806b1a05306ba3bb601">  813</a></span>        <a class="code hl_function" href="classnz_1_1nodes_1_1loss_1_1_binary_cross_entropy_node.html#a79a7fb04d377c806b1a05306ba3bb601">BinaryCrossEntropyNode::BinaryCrossEntropyNode</a>(<a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input1, <a class="code hl_class" href="classnz_1_1nodes_1_1_node.html">Node</a>* input2) :</div>
<div class="line"><a id="l00814" name="l00814"></a><span class="lineno">  814</span>            OutputNode(input1) {</div>
<div class="line"><a id="l00815" name="l00815"></a><span class="lineno">  815</span>            <span class="keywordflow">if</span> (input1-&gt;output-&gt;shape() != input2-&gt;output-&gt;shape()) {</div>
<div class="line"><a id="l00816" name="l00816"></a><span class="lineno">  816</span>                throw std::invalid_argument(<span class="stringliteral">&quot;input1 and input2 should have the same shape&quot;</span>);</div>
<div class="line"><a id="l00817" name="l00817"></a><span class="lineno">  817</span>            }</div>
<div class="line"><a id="l00818" name="l00818"></a><span class="lineno">  818</span>            inputs.push_back(input2);</div>
<div class="line"><a id="l00819" name="l00819"></a><span class="lineno">  819</span>            type = <span class="stringliteral">&quot;BinaryCrossEntropy&quot;</span>;</div>
<div class="line"><a id="l00820" name="l00820"></a><span class="lineno">  820</span>        }</div>
</div>
<div class="line"><a id="l00821" name="l00821"></a><span class="lineno">  821</span> </div>
<div class="foldopen" id="foldopen00822" data-start="{" data-end="}">
<div class="line"><a id="l00822" name="l00822"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1loss_1_1_binary_cross_entropy_node.html#afccd1a1a1207379dbfb648d0cbc3aab4">  822</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1loss_1_1_binary_cross_entropy_node.html#afccd1a1a1207379dbfb648d0cbc3aab4">BinaryCrossEntropyNode::forward</a>() {</div>
<div class="line"><a id="l00823" name="l00823"></a><span class="lineno">  823</span>            OutputNode::forward();</div>
<div class="line"><a id="l00824" name="l00824"></a><span class="lineno">  824</span>            <span class="keyword">const</span> dim3 block(256);</div>
<div class="line"><a id="l00825" name="l00825"></a><span class="lineno">  825</span>            <span class="keyword">const</span> dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00826" name="l00826"></a><span class="lineno">  826</span>            <span class="keywordtype">float</span>* result;</div>
<div class="line"><a id="l00827" name="l00827"></a><span class="lineno">  827</span>            <span class="keyword">auto</span>* result_host = <span class="keyword">static_cast&lt;</span><span class="keywordtype">float</span>*<span class="keyword">&gt;</span>(malloc(grid.x * <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>)));</div>
<div class="line"><a id="l00828" name="l00828"></a><span class="lineno">  828</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;Tensor::value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">malloc</a>(&amp;result, grid.x * <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>));</div>
<div class="line"><a id="l00829" name="l00829"></a><span class="lineno">  829</span>            <a class="code hl_function" href="namespacenz_1_1krnl.html#abf927faf0950fbc215564c67b8ac57be">BinaryCrossEntropy</a>(grid, block, block.x / WARP_SIZE * <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>), result, inputs[0]-&gt;output-&gt;data(),</div>
<div class="line"><a id="l00830" name="l00830"></a><span class="lineno">  830</span>                               inputs[1]-&gt;output-&gt;data(), output-&gt;size());</div>
<div class="line"><a id="l00831" name="l00831"></a><span class="lineno">  831</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;Tensor::value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">memcpy</a>(</div>
<div class="line"><a id="l00832" name="l00832"></a><span class="lineno">  832</span>                result_host, result, grid.x * <span class="keyword">sizeof</span>(<span class="keywordtype">float</span>), cudaMemcpyDeviceToHost);</div>
<div class="line"><a id="l00833" name="l00833"></a><span class="lineno">  833</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;Tensor::value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">syncData</a>(result_host);</div>
<div class="line"><a id="l00834" name="l00834"></a><span class="lineno">  834</span>            <span class="keywordflow">for</span> (<span class="keywordtype">int</span> i = 0; i &lt; grid.x; i++) {</div>
<div class="line"><a id="l00835" name="l00835"></a><span class="lineno">  835</span>                loss += result_host[i];</div>
<div class="line"><a id="l00836" name="l00836"></a><span class="lineno">  836</span>            }</div>
<div class="line"><a id="l00837" name="l00837"></a><span class="lineno">  837</span>            std::cout &lt;&lt; <span class="stringliteral">&quot;TEST&quot;</span> &lt;&lt; std::endl;</div>
<div class="line"><a id="l00838" name="l00838"></a><span class="lineno">  838</span>            <a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">cuStrm::StreamManager&lt;Tensor::value_type&gt;::Instance</a>().<a class="code hl_function" href="classnz_1_1cu_strm_1_1_stream_manager.html#a785cf34395067f425e032d9bd5e1fa20">free</a>(result);</div>
<div class="line"><a id="l00839" name="l00839"></a><span class="lineno">  839</span>            free(result_host);</div>
<div class="line"><a id="l00840" name="l00840"></a><span class="lineno">  840</span>        }</div>
</div>
<div class="line"><a id="l00841" name="l00841"></a><span class="lineno">  841</span> </div>
<div class="foldopen" id="foldopen00842" data-start="{" data-end="}">
<div class="line"><a id="l00842" name="l00842"></a><span class="lineno"><a class="line" href="classnz_1_1nodes_1_1loss_1_1_binary_cross_entropy_node.html#a868b2f5886b2e2adee12439ad50ca91a">  842</a></span>        <span class="keywordtype">void</span> <a class="code hl_function" href="classnz_1_1nodes_1_1loss_1_1_binary_cross_entropy_node.html#a868b2f5886b2e2adee12439ad50ca91a">BinaryCrossEntropyNode::backward</a>() {</div>
<div class="line"><a id="l00843" name="l00843"></a><span class="lineno">  843</span>            <span class="keywordflow">if</span> (output-&gt;requiresGrad()) {</div>
<div class="line"><a id="l00844" name="l00844"></a><span class="lineno">  844</span>                dim3 block(256);</div>
<div class="line"><a id="l00845" name="l00845"></a><span class="lineno">  845</span>                dim3 grid((output-&gt;size() + block.x - 1) / block.x);</div>
<div class="line"><a id="l00846" name="l00846"></a><span class="lineno">  846</span>                <a class="code hl_function" href="namespacenz_1_1krnl.html#a1fc3d553947a5cad87f29989f9d9465d">BCEBackward</a>(grid, block, output-&gt;grad(), inputs[0]-&gt;output-&gt;data(), inputs[1]-&gt;output-&gt;data(),</div>
<div class="line"><a id="l00847" name="l00847"></a><span class="lineno">  847</span>                            output-&gt;size());</div>
<div class="line"><a id="l00848" name="l00848"></a><span class="lineno">  848</span>            }</div>
<div class="line"><a id="l00849" name="l00849"></a><span class="lineno">  849</span>        }</div>
</div>
<div class="line"><a id="l00850" name="l00850"></a><span class="lineno">  850</span>    }</div>
<div class="line"><a id="l00851" name="l00851"></a><span class="lineno">  851</span>}</div>
<div class="ttc" id="a_nodes_8cuh_html"><div class="ttname"><a href="_nodes_8cuh.html">Nodes.cuh</a></div><div class="ttdoc">Declaration of the Node class and various derived node classes for neural network operations.</div></div>
<div class="ttc" id="a_operation_kernels_8cuh_html"><div class="ttname"><a href="_operation_kernels_8cuh.html">OperationKernels.cuh</a></div><div class="ttdoc">CUDA Kernel Definitions for High-Performance Tensor Operations.</div></div>
<div class="ttc" id="aclassnz_1_1cu_strm_1_1_stream_manager_html_a785cf34395067f425e032d9bd5e1fa20"><div class="ttname"><a href="classnz_1_1cu_strm_1_1_stream_manager.html#a785cf34395067f425e032d9bd5e1fa20">nz::cuStrm::StreamManager::free</a></div><div class="ttdeci">void free(T *data)</div><div class="ttdoc">Frees the CUDA device memory pointed to by the given pointer.</div><div class="ttdef"><b>Definition</b> <a href="_stream_manager_8cuh_source.html#l00263">StreamManager.cuh:263</a></div></div>
<div class="ttc" id="aclassnz_1_1cu_strm_1_1_stream_manager_html_a97f78a2d43f6e0508c82d4f3b629de96"><div class="ttname"><a href="classnz_1_1cu_strm_1_1_stream_manager.html#a97f78a2d43f6e0508c82d4f3b629de96">nz::cuStrm::StreamManager::malloc</a></div><div class="ttdeci">void malloc(T **data, const size_t size)</div><div class="ttdoc">Asynchronously allocates device memory for type-specific data with stream-ordered dependency tracking...</div><div class="ttdef"><b>Definition</b> <a href="_stream_manager_8cuh_source.html#l00230">StreamManager.cuh:230</a></div></div>
<div class="ttc" id="aclassnz_1_1cu_strm_1_1_stream_manager_html_ab4b2eb422e0e1ee44bdfdc0eb94457ce"><div class="ttname"><a href="classnz_1_1cu_strm_1_1_stream_manager.html#ab4b2eb422e0e1ee44bdfdc0eb94457ce">nz::cuStrm::StreamManager::Instance</a></div><div class="ttdeci">static StreamManager &amp; Instance()</div><div class="ttdoc">Returns a reference to the singleton instance of the StreamManager.</div><div class="ttdef"><b>Definition</b> <a href="_stream_manager_8cuh_source.html#l00154">StreamManager.cuh:154</a></div></div>
<div class="ttc" id="aclassnz_1_1cu_strm_1_1_stream_manager_html_abe439fa00c0bd369c0b2345b095ed5af"><div class="ttname"><a href="classnz_1_1cu_strm_1_1_stream_manager.html#abe439fa00c0bd369c0b2345b095ed5af">nz::cuStrm::StreamManager::syncData</a></div><div class="ttdeci">void syncData(T *data)</div><div class="ttdoc">Synchronizes host thread with completion events for a specific data object.</div><div class="ttdef"><b>Definition</b> <a href="_stream_manager_8cuh_source.html#l00714">StreamManager.cuh:714</a></div></div>
<div class="ttc" id="aclassnz_1_1cu_strm_1_1_stream_manager_html_afa38d5c6db0e6b48c8f74ce8ad0df2bc"><div class="ttname"><a href="classnz_1_1cu_strm_1_1_stream_manager.html#afa38d5c6db0e6b48c8f74ce8ad0df2bc">nz::cuStrm::StreamManager::memcpy</a></div><div class="ttdeci">void memcpy(T *dst, T *src, const size_t size, const cudaMemcpyKind kind)</div><div class="ttdoc">Asynchronously copies data between CUDA device and host memory based on the specified memory copy kin...</div><div class="ttdef"><b>Definition</b> <a href="_stream_manager_8cuh_source.html#l00391">StreamManager.cuh:391</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_dimension_html"><div class="ttname"><a href="classnz_1_1data_1_1_dimension.html">nz::data::Dimension</a></div><div class="ttdoc">Represents a multi - dimensional shape, typically used in deep learning for tensor dimensions.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cuh_source.html#l00057">Dimension.cuh:57</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_dimension_html_a073622bb031999163987ccf77f8edfb2"><div class="ttname"><a href="classnz_1_1data_1_1_dimension.html#a073622bb031999163987ccf77f8edfb2">nz::data::Dimension::size</a></div><div class="ttdeci">size_t size() const</div><div class="ttdoc">Calculates the total number of elements in the Dimension object.</div><div class="ttdef"><b>Definition</b> <a href="_dimension_8cu_source.html#l00036">Dimension.cu:36</a></div></div>
<div class="ttc" id="aclassnz_1_1data_1_1_tensor_html"><div class="ttname"><a href="classnz_1_1data_1_1_tensor.html">nz::data::Tensor</a></div><div class="ttdoc">A class for representing and manipulating multidimensional arrays (tensors) in GPU memory.</div><div class="ttdef"><b>Definition</b> <a href="_tensor_8cuh_source.html#l00134">Tensor.cuh:134</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1_node_html"><div class="ttname"><a href="classnz_1_1nodes_1_1_node.html">nz::nodes::Node</a></div><div class="ttdoc">Base class for nodes in a neural network or computational graph.</div><div class="ttdef"><b>Definition</b> <a href="_nodes_8cuh_source.html#l00114">Nodes.cuh:114</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1_node_html_a687ee9c34eb61f8f28caa201ca42696e"><div class="ttname"><a href="classnz_1_1nodes_1_1_node.html#a687ee9c34eb61f8f28caa201ca42696e">nz::nodes::Node::print</a></div><div class="ttdeci">virtual void print(std::ostream &amp;os) const</div><div class="ttdoc">Prints the type, data, and gradient of the node.</div><div class="ttdef"><b>Definition</b> <a href="#l00010">Nodes.cu:10</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1_node_html_a9b85913e12422bb4ac2fff483427bb47"><div class="ttname"><a href="classnz_1_1nodes_1_1_node.html#a9b85913e12422bb4ac2fff483427bb47">nz::nodes::Node::dataInject</a></div><div class="ttdeci">void dataInject(Tensor::value_type *data, bool grad=false) const</div><div class="ttdoc">Injects data into a relevant tensor object, optionally setting its gradient requirement.</div><div class="ttdef"><b>Definition</b> <a href="#l00015">Nodes.cu:15</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_add_node_html_aacd0de4600132791c8da7860dba3e43c"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_add_node.html#aacd0de4600132791c8da7860dba3e43c">nz::nodes::calc::AddNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Backward pass for the AddNode to propagate gradients.</div><div class="ttdef"><b>Definition</b> <a href="#l00099">Nodes.cu:99</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_add_node_html_abf5d0c2b9827bfb8fd1f3a004db80175"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_add_node.html#abf5d0c2b9827bfb8fd1f3a004db80175">nz::nodes::calc::AddNode::AddNode</a></div><div class="ttdeci">AddNode(Node *input_left, Node *input_right)</div><div class="ttdoc">Constructor to initialize an AddNode with two input nodes for element-wise addition.</div><div class="ttdef"><b>Definition</b> <a href="#l00079">Nodes.cu:79</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_add_node_html_adcbcffc97ede105ec64c7360377b9af3"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_add_node.html#adcbcffc97ede105ec64c7360377b9af3">nz::nodes::calc::AddNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Forward pass for the AddNode to perform element-wise addition.</div><div class="ttdef"><b>Definition</b> <a href="#l00095">Nodes.cu:95</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_average_pooling_node_html_a12dcec668190c3f466f5bd5e0b3f96a7"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_average_pooling_node.html#a12dcec668190c3f466f5bd5e0b3f96a7">nz::nodes::calc::AveragePoolingNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Performs the backward pass of the average pooling operation.</div><div class="ttdef"><b>Definition</b> <a href="#l00684">Nodes.cu:684</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_average_pooling_node_html_ab206cec144b3b88af4a90c0eb0f5733c"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_average_pooling_node.html#ab206cec144b3b88af4a90c0eb0f5733c">nz::nodes::calc::AveragePoolingNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Performs the backward pass of the average pooling operation.</div><div class="ttdef"><b>Definition</b> <a href="#l00678">Nodes.cu:678</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_average_pooling_node_html_add1055d87bda108863a4c3bf8dca5055"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_average_pooling_node.html#add1055d87bda108863a4c3bf8dca5055">nz::nodes::calc::AveragePoolingNode::AveragePoolingNode</a></div><div class="ttdeci">AveragePoolingNode(Node *input, Tensor::size_type poolSize, Tensor::size_type stride, Tensor::size_type padding)</div><div class="ttdoc">Constructs an AveragePoolingNode object.</div><div class="ttdef"><b>Definition</b> <a href="#l00667">Nodes.cu:667</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_col2_img_node_html_a247f00c842e0420bac145f90c688a74a"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_col2_img_node.html#a247f00c842e0420bac145f90c688a74a">nz::nodes::calc::Col2ImgNode::Col2ImgNode</a></div><div class="ttdeci">Col2ImgNode(Node *input, Tensor::size_type outputHeight, Tensor::size_type outputWidth)</div><div class="ttdoc">Constructor for the Col2ImgNode class.</div><div class="ttdef"><b>Definition</b> <a href="#l00642">Nodes.cu:642</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_col2_img_node_html_a372c237486b96cadfbc71fe7e3a16bdd"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_col2_img_node.html#a372c237486b96cadfbc71fe7e3a16bdd">nz::nodes::calc::Col2ImgNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Performs the backward propagation operation in the Col2ImgNode.</div><div class="ttdef"><b>Definition</b> <a href="#l00660">Nodes.cu:660</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_col2_img_node_html_ae70df2f92889693ba699051af3de703f"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_col2_img_node.html#ae70df2f92889693ba699051af3de703f">nz::nodes::calc::Col2ImgNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Performs the forward propagation operation in the Col2ImgNode.</div><div class="ttdef"><b>Definition</b> <a href="#l00655">Nodes.cu:655</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_e_l_u_node_html_a167cd647d2a6e3f273c250f15562a317"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_e_l_u_node.html#a167cd647d2a6e3f273c250f15562a317">nz::nodes::calc::ELUNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Backward pass for the ELUNode to compute gradients.</div><div class="ttdef"><b>Definition</b> <a href="#l00467">Nodes.cu:467</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_e_l_u_node_html_a1dc0572d0688d2e65912b367286ac6ad"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_e_l_u_node.html#a1dc0572d0688d2e65912b367286ac6ad">nz::nodes::calc::ELUNode::ELUNode</a></div><div class="ttdeci">ELUNode(Node *input, Tensor::value_type alpha=1.0f)</div><div class="ttdoc">Constructor to initialize an ELUNode for applying the ELU activation function.</div><div class="ttdef"><b>Definition</b> <a href="#l00453">Nodes.cu:453</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_e_l_u_node_html_acd8b07d5fbd5a920d58d55a72a9ff092"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_e_l_u_node.html#acd8b07d5fbd5a920d58d55a72a9ff092">nz::nodes::calc::ELUNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Forward pass for the ELUNode to apply the ELU activation function.</div><div class="ttdef"><b>Definition</b> <a href="#l00461">Nodes.cu:461</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_expand_node_html_a78874b46ee54a4a11f5d0ae2f79409c0"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_expand_node.html#a78874b46ee54a4a11f5d0ae2f79409c0">nz::nodes::calc::ExpandNode::ExpandNode</a></div><div class="ttdeci">ExpandNode(Node *input, Tensor::size_type newBatch)</div><div class="ttdoc">Constructs an ExpandNode object.</div><div class="ttdef"><b>Definition</b> <a href="#l00573">Nodes.cu:573</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_expand_node_html_a883419ee48eca406c2deb6939aa1a546"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_expand_node.html#a883419ee48eca406c2deb6939aa1a546">nz::nodes::calc::ExpandNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Performs the forward propagation for the ExpandNode.</div><div class="ttdef"><b>Definition</b> <a href="#l00585">Nodes.cu:585</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_expand_node_html_aac692ee5bf79df2148b9595030b46585"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_expand_node.html#aac692ee5bf79df2148b9595030b46585">nz::nodes::calc::ExpandNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Performs the backward propagation for the ExpandNode.</div><div class="ttdef"><b>Definition</b> <a href="#l00594">Nodes.cu:594</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_global_avg_pool_node_html_a7a075619e9c976875118783bbdac8739"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_global_avg_pool_node.html#a7a075619e9c976875118783bbdac8739">nz::nodes::calc::GlobalAvgPoolNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Performs the backward pass of the global average pooling operation.</div><div class="ttdef"><b>Definition</b> <a href="#l00709">Nodes.cu:709</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_global_avg_pool_node_html_ab1e87b4e3649f8a67bfc5a83bb3600e0"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_global_avg_pool_node.html#ab1e87b4e3649f8a67bfc5a83bb3600e0">nz::nodes::calc::GlobalAvgPoolNode::GlobalAvgPoolNode</a></div><div class="ttdeci">GlobalAvgPoolNode(Node *input)</div><div class="ttdoc">Constructs a GlobalAvgPoolNode object.</div><div class="ttdef"><b>Definition</b> <a href="#l00692">Nodes.cu:692</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_global_avg_pool_node_html_ad8b2bb2ce47ab3c227f8c33f3a19fb91"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_global_avg_pool_node.html#ad8b2bb2ce47ab3c227f8c33f3a19fb91">nz::nodes::calc::GlobalAvgPoolNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Performs the forward pass of the global average pooling operation.</div><div class="ttdef"><b>Definition</b> <a href="#l00700">Nodes.cu:700</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_global_max_pool_node_html_a2242a5196a07fdedc0a66ef457f57487"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_global_max_pool_node.html#a2242a5196a07fdedc0a66ef457f57487">nz::nodes::calc::GlobalMaxPoolNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Performs the backward pass of the global max - pooling operation.</div><div class="ttdef"><b>Definition</b> <a href="#l00761">Nodes.cu:761</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_global_max_pool_node_html_a64613a073db56dbc1526aeb226497a73"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_global_max_pool_node.html#a64613a073db56dbc1526aeb226497a73">nz::nodes::calc::GlobalMaxPoolNode::GlobalMaxPoolNode</a></div><div class="ttdeci">GlobalMaxPoolNode(Node *input)</div><div class="ttdoc">Constructs a GlobalMaxPoolNode object.</div><div class="ttdef"><b>Definition</b> <a href="#l00745">Nodes.cu:745</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_global_max_pool_node_html_ae35d13815fe6cb9bb46abe235e9469aa"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_global_max_pool_node.html#ae35d13815fe6cb9bb46abe235e9469aa">nz::nodes::calc::GlobalMaxPoolNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Performs the forward pass of the global max - pooling operation.</div><div class="ttdef"><b>Definition</b> <a href="#l00753">Nodes.cu:753</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_hard_sigmoid_node_html_a97590995aa192807d96a856ee2bcd71f"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_hard_sigmoid_node.html#a97590995aa192807d96a856ee2bcd71f">nz::nodes::calc::HardSigmoidNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Forward pass for the HardSigmoidNode to apply the Hard Sigmoid activation function.</div><div class="ttdef"><b>Definition</b> <a href="#l00485">Nodes.cu:485</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_hard_sigmoid_node_html_a9fad60d7a07f6296aa5ce13acd6511d2"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_hard_sigmoid_node.html#a9fad60d7a07f6296aa5ce13acd6511d2">nz::nodes::calc::HardSigmoidNode::HardSigmoidNode</a></div><div class="ttdeci">HardSigmoidNode(Node *input, Tensor::value_type alpha=0.2f, Tensor::value_type beta=0.5f)</div><div class="ttdoc">Constructor to initialize a HardSigmoidNode for applying the Hard Sigmoid activation function.</div><div class="ttdef"><b>Definition</b> <a href="#l00476">Nodes.cu:476</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_hard_sigmoid_node_html_ad977c6a8c49252de4038f8ac08beed3c"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_hard_sigmoid_node.html#ad977c6a8c49252de4038f8ac08beed3c">nz::nodes::calc::HardSigmoidNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Backward pass for the HardSigmoidNode to compute gradients.</div><div class="ttdef"><b>Definition</b> <a href="#l00491">Nodes.cu:491</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_hard_swish_node_html_a014a289c458494bf3bf02f266c129205"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_hard_swish_node.html#a014a289c458494bf3bf02f266c129205">nz::nodes::calc::HardSwishNode::HardSwishNode</a></div><div class="ttdeci">HardSwishNode(Node *input, Tensor::value_type alpha=1.0f, Tensor::value_type beta=0.5f)</div><div class="ttdoc">Constructor to initialize a HardSwishNode for applying the Hard Swish activation function.</div><div class="ttdef"><b>Definition</b> <a href="#l00500">Nodes.cu:500</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_hard_swish_node_html_ace2cc92220449c5425a75d8e7f35ff42"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_hard_swish_node.html#ace2cc92220449c5425a75d8e7f35ff42">nz::nodes::calc::HardSwishNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Backward pass for the HardSwishNode to compute gradients.</div><div class="ttdef"><b>Definition</b> <a href="#l00515">Nodes.cu:515</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_hard_swish_node_html_afd48643b49c76a9c38b0bfc0c4a502f1"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_hard_swish_node.html#afd48643b49c76a9c38b0bfc0c4a502f1">nz::nodes::calc::HardSwishNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Forward pass for the HardSwishNode to apply the Hard Swish activation function.</div><div class="ttdef"><b>Definition</b> <a href="#l00509">Nodes.cu:509</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_img2_col_node_html_a199c6e1750035b8b9b4489de664b3ad3"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_img2_col_node.html#a199c6e1750035b8b9b4489de664b3ad3">nz::nodes::calc::Img2ColNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Performs the forward propagation for the Img2ColNode.</div><div class="ttdef"><b>Definition</b> <a href="#l00624">Nodes.cu:624</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_img2_col_node_html_a900bfad8e2c2706ffc9cf5cf20dee6dd"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_img2_col_node.html#a900bfad8e2c2706ffc9cf5cf20dee6dd">nz::nodes::calc::Img2ColNode::Img2ColNode</a></div><div class="ttdeci">Img2ColNode(Node *input, Tensor::size_type kernelHeight, Tensor::size_type kernelWidth, Tensor::size_type stride, Tensor::size_type padding)</div><div class="ttdoc">Constructor for the Img2ColNode class.</div><div class="ttdef"><b>Definition</b> <a href="#l00605">Nodes.cu:605</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_img2_col_node_html_a905c98a496d0106aed9af4e71205f653"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_img2_col_node.html#a905c98a496d0106aed9af4e71205f653">nz::nodes::calc::Img2ColNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Performs the backward propagation for the Img2ColNode.</div><div class="ttdef"><b>Definition</b> <a href="#l00632">Nodes.cu:632</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_leaky_re_l_u_node_html_a287aebe2ce3437d393fb1ac1b1119d25"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_leaky_re_l_u_node.html#a287aebe2ce3437d393fb1ac1b1119d25">nz::nodes::calc::LeakyReLUNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Forward pass for the LeakyReLUNode to apply the Leaky ReLU activation function.</div><div class="ttdef"><b>Definition</b> <a href="#l00416">Nodes.cu:416</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_leaky_re_l_u_node_html_aa3fe1c9e74733c8c5e15e71ec14143f0"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_leaky_re_l_u_node.html#aa3fe1c9e74733c8c5e15e71ec14143f0">nz::nodes::calc::LeakyReLUNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Backward pass for the LeakyReLUNode to compute gradients.</div><div class="ttdef"><b>Definition</b> <a href="#l00422">Nodes.cu:422</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_leaky_re_l_u_node_html_ae26fe5bd4367d39c5b054ecd0f4621fa"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_leaky_re_l_u_node.html#ae26fe5bd4367d39c5b054ecd0f4621fa">nz::nodes::calc::LeakyReLUNode::LeakyReLUNode</a></div><div class="ttdeci">LeakyReLUNode(Node *input, Tensor::value_type alpha=0.01f)</div><div class="ttdoc">Constructor to initialize a LeakyReLUNode for applying the Leaky ReLU activation function.</div><div class="ttdef"><b>Definition</b> <a href="#l00408">Nodes.cu:408</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_mat_mul_node_html_a493c723e88870bdc46c9b30b74b1e173"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_mat_mul_node.html#a493c723e88870bdc46c9b30b74b1e173">nz::nodes::calc::MatMulNode::MatMulNode</a></div><div class="ttdeci">MatMulNode(Node *input_left, Node *input_right)</div><div class="ttdoc">Constructor to initialize a MatMulNode for matrix multiplication.</div><div class="ttdef"><b>Definition</b> <a href="#l00148">Nodes.cu:148</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_mat_mul_node_html_a4d1ec1a90036ff16358c5f83123bac67"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_mat_mul_node.html#a4d1ec1a90036ff16358c5f83123bac67">nz::nodes::calc::MatMulNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Forward pass for the MatMulNode to perform matrix multiplication.</div><div class="ttdef"><b>Definition</b> <a href="#l00165">Nodes.cu:165</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_mat_mul_node_html_ab644a874feb6a620ad31d37ca20525fd"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_mat_mul_node.html#ab644a874feb6a620ad31d37ca20525fd">nz::nodes::calc::MatMulNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Backward pass for the MatMulNode to propagate gradients.</div><div class="ttdef"><b>Definition</b> <a href="#l00169">Nodes.cu:169</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_max_pooling_node_html_a2dd8c57551f76d957ef97121c6df2adc"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_max_pooling_node.html#a2dd8c57551f76d957ef97121c6df2adc">nz::nodes::calc::MaxPoolingNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Performs the forward pass of the max - pooling operation.</div><div class="ttdef"><b>Definition</b> <a href="#l00731">Nodes.cu:731</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_max_pooling_node_html_a56018af1d41b8058deabe63c38e03c0d"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_max_pooling_node.html#a56018af1d41b8058deabe63c38e03c0d">nz::nodes::calc::MaxPoolingNode::MaxPoolingNode</a></div><div class="ttdeci">MaxPoolingNode(Node *input, Tensor::size_type poolSize, Tensor::size_type stride, Tensor::size_type padding)</div><div class="ttdoc">Constructs a MaxPoolingNode object.</div><div class="ttdef"><b>Definition</b> <a href="#l00716">Nodes.cu:716</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_max_pooling_node_html_ae0a0a0b7303101c4417564babf0fcd5f"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_max_pooling_node.html#ae0a0a0b7303101c4417564babf0fcd5f">nz::nodes::calc::MaxPoolingNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Performs the backward pass of the max - pooling operation.</div><div class="ttdef"><b>Definition</b> <a href="#l00737">Nodes.cu:737</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_re_l_u_node_html_a78a8e2e5cf13d31956e2367d923efa53"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_re_l_u_node.html#a78a8e2e5cf13d31956e2367d923efa53">nz::nodes::calc::ReLUNode::ReLUNode</a></div><div class="ttdeci">ReLUNode(Node *input)</div><div class="ttdoc">Constructor to initialize a ReLUNode for applying the ReLU activation function.</div><div class="ttdef"><b>Definition</b> <a href="#l00344">Nodes.cu:344</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_re_l_u_node_html_ae8e78ee766f1e4845c6a110464e077f7"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_re_l_u_node.html#ae8e78ee766f1e4845c6a110464e077f7">nz::nodes::calc::ReLUNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Forward pass for the ReLUNode to apply the ReLU activation function.</div><div class="ttdef"><b>Definition</b> <a href="#l00351">Nodes.cu:351</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_re_l_u_node_html_af06dd1eadec4b3616c7c9e655820b1b8"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_re_l_u_node.html#af06dd1eadec4b3616c7c9e655820b1b8">nz::nodes::calc::ReLUNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Backward pass for the ReLUNode to compute gradients.</div><div class="ttdef"><b>Definition</b> <a href="#l00357">Nodes.cu:357</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_reshape_node_html_a28c90cc1c5dd3837dcbde0c1abc841d5"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_reshape_node.html#a28c90cc1c5dd3837dcbde0c1abc841d5">nz::nodes::calc::ReshapeNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Performs the backward propagation for the ReshapeNode.</div><div class="ttdef"><b>Definition</b> <a href="#l00565">Nodes.cu:565</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_reshape_node_html_a8f9c9e9dbbe4db8d9420b9928ab369f1"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_reshape_node.html#a8f9c9e9dbbe4db8d9420b9928ab369f1">nz::nodes::calc::ReshapeNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Performs the forward pass operation of the ReshapeNode.</div><div class="ttdef"><b>Definition</b> <a href="#l00559">Nodes.cu:559</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_reshape_node_html_adb97b80637a53a9e1b9776f8fcae8ed7"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_reshape_node.html#adb97b80637a53a9e1b9776f8fcae8ed7">nz::nodes::calc::ReshapeNode::ReshapeNode</a></div><div class="ttdeci">ReshapeNode(Node *input, const Tensor::shape_type &amp;newShape)</div><div class="ttdoc">Constructs a ReshapeNode object to reshape the input tensor.</div><div class="ttdef"><b>Definition</b> <a href="#l00550">Nodes.cu:550</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_scalar_add_node_html_a453eed787a8161b36410bef2ba8b0a75"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_scalar_add_node.html#a453eed787a8161b36410bef2ba8b0a75">nz::nodes::calc::ScalarAddNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Backward pass for the ScalarAddNode to propagate gradients.</div><div class="ttdef"><b>Definition</b> <a href="#l00251">Nodes.cu:251</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_scalar_add_node_html_a83edb10337111d0ffe7140a154954a3b"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_scalar_add_node.html#a83edb10337111d0ffe7140a154954a3b">nz::nodes::calc::ScalarAddNode::ScalarAddNode</a></div><div class="ttdeci">ScalarAddNode(Node *input, Tensor::value_type scalar)</div><div class="ttdoc">Constructor to initialize a ScalarAddNode for scalar addition.</div><div class="ttdef"><b>Definition</b> <a href="#l00235">Nodes.cu:235</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_scalar_add_node_html_ad1162e693cd13ee6e9e4f7cab27e4a31"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_scalar_add_node.html#ad1162e693cd13ee6e9e4f7cab27e4a31">nz::nodes::calc::ScalarAddNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Forward pass for the ScalarAddNode to perform scalar addition.</div><div class="ttdef"><b>Definition</b> <a href="#l00245">Nodes.cu:245</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_scalar_div_node_html_a4728d1f10d35d7e71b11acd32ee1a26d"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_scalar_div_node.html#a4728d1f10d35d7e71b11acd32ee1a26d">nz::nodes::calc::ScalarDivNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Forward pass for the ScalarDivNode to perform scalar division.</div><div class="ttdef"><b>Definition</b> <a href="#l00221">Nodes.cu:221</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_scalar_div_node_html_a9b72dc5618e8e11790756c91116719e4"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_scalar_div_node.html#a9b72dc5618e8e11790756c91116719e4">nz::nodes::calc::ScalarDivNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Backward pass for the ScalarDivNode to propagate gradients.</div><div class="ttdef"><b>Definition</b> <a href="#l00227">Nodes.cu:227</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_scalar_div_node_html_ac04d8d6de4becf4e1c7911e99c131b7d"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_scalar_div_node.html#ac04d8d6de4becf4e1c7911e99c131b7d">nz::nodes::calc::ScalarDivNode::ScalarDivNode</a></div><div class="ttdeci">ScalarDivNode(Node *input, Tensor::value_type scalar)</div><div class="ttdoc">Constructor to initialize a ScalarDivNode for scalar division.</div><div class="ttdef"><b>Definition</b> <a href="#l00208">Nodes.cu:208</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_scalar_mul_node_html_a074fe78c03e0b62c5e69b6a25b6b4c24"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_scalar_mul_node.html#a074fe78c03e0b62c5e69b6a25b6b4c24">nz::nodes::calc::ScalarMulNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Backward pass for the ScalarMulNode to propagate gradients.</div><div class="ttdef"><b>Definition</b> <a href="#l00200">Nodes.cu:200</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_scalar_mul_node_html_a32ca702895991f74b867c06e1807c96e"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_scalar_mul_node.html#a32ca702895991f74b867c06e1807c96e">nz::nodes::calc::ScalarMulNode::ScalarMulNode</a></div><div class="ttdeci">ScalarMulNode(Node *input, Tensor::value_type scalar)</div><div class="ttdoc">Constructor to initialize a ScalarMulNode for scalar multiplication.</div><div class="ttdef"><b>Definition</b> <a href="#l00184">Nodes.cu:184</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_scalar_mul_node_html_af96c94d5a91e2ee3bd97113992c829ca"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_scalar_mul_node.html#af96c94d5a91e2ee3bd97113992c829ca">nz::nodes::calc::ScalarMulNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Forward pass for the ScalarMulNode to perform scalar multiplication.</div><div class="ttdef"><b>Definition</b> <a href="#l00194">Nodes.cu:194</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_scalar_sub_node_html_a27b61f5a960545b810cf3151fe65adf6"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_scalar_sub_node.html#a27b61f5a960545b810cf3151fe65adf6">nz::nodes::calc::ScalarSubNode::ScalarSubNode</a></div><div class="ttdeci">ScalarSubNode(Node *input, Tensor::value_type scalar)</div><div class="ttdoc">Constructor to initialize a ScalarSubNode for scalar subtraction.</div><div class="ttdef"><b>Definition</b> <a href="#l00259">Nodes.cu:259</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_scalar_sub_node_html_a7880d18811e20c3ec34b1417a28d697e"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_scalar_sub_node.html#a7880d18811e20c3ec34b1417a28d697e">nz::nodes::calc::ScalarSubNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Backward pass for the ScalarSubNode to propagate gradients.</div><div class="ttdef"><b>Definition</b> <a href="#l00275">Nodes.cu:275</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_scalar_sub_node_html_ac5d375db4c17885e597c4dcca9d0a318"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_scalar_sub_node.html#ac5d375db4c17885e597c4dcca9d0a318">nz::nodes::calc::ScalarSubNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Forward pass for the ScalarSubNode to perform scalar subtraction.</div><div class="ttdef"><b>Definition</b> <a href="#l00269">Nodes.cu:269</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_sigmoid_node_html_a05fe16b3ecde344d9463efabf1318115"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_sigmoid_node.html#a05fe16b3ecde344d9463efabf1318115">nz::nodes::calc::SigmoidNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Backward pass for the SigmoidNode to compute gradients.</div><div class="ttdef"><b>Definition</b> <a href="#l00379">Nodes.cu:379</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_sigmoid_node_html_aa24b02f6d79fda31e6ad150879ed2bbb"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_sigmoid_node.html#aa24b02f6d79fda31e6ad150879ed2bbb">nz::nodes::calc::SigmoidNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Forward pass for the SigmoidNode to apply the Sigmoid activation function.</div><div class="ttdef"><b>Definition</b> <a href="#l00373">Nodes.cu:373</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_sigmoid_node_html_aad3fb8b89ac7cde6d70c464e06c35ade"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_sigmoid_node.html#aad3fb8b89ac7cde6d70c464e06c35ade">nz::nodes::calc::SigmoidNode::SigmoidNode</a></div><div class="ttdeci">SigmoidNode(Node *input)</div><div class="ttdoc">Constructor to initialize a SigmoidNode for applying the Sigmoid activation function.</div><div class="ttdef"><b>Definition</b> <a href="#l00366">Nodes.cu:366</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_softmax_node_html_a6bd70cb3436435bac2055e86dfdb078b"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_softmax_node.html#a6bd70cb3436435bac2055e86dfdb078b">nz::nodes::calc::SoftmaxNode::SoftmaxNode</a></div><div class="ttdeci">SoftmaxNode(Node *input)</div><div class="ttdoc">Constructor to initialize a SoftmaxNode for applying the Softmax activation function.</div><div class="ttdef"><b>Definition</b> <a href="#l00524">Nodes.cu:524</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_softmax_node_html_a93f7d936ff487db8e7dceb6ee0cdc38e"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_softmax_node.html#a93f7d936ff487db8e7dceb6ee0cdc38e">nz::nodes::calc::SoftmaxNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Performs the forward pass of the Softmax operation.</div><div class="ttdef"><b>Definition</b> <a href="#l00534">Nodes.cu:534</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_softmax_node_html_aa991e3bde7a3a5edbee62fab1cabba23"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_softmax_node.html#aa991e3bde7a3a5edbee62fab1cabba23">nz::nodes::calc::SoftmaxNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Performs the backward pass of the Softmax operation.</div><div class="ttdef"><b>Definition</b> <a href="#l00538">Nodes.cu:538</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_sub_node_html_a6ba6a63da4e869f8f0004896d01fe3f1"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_sub_node.html#a6ba6a63da4e869f8f0004896d01fe3f1">nz::nodes::calc::SubNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Forward pass for the SubNode to perform tensor subtraction.</div><div class="ttdef"><b>Definition</b> <a href="#l00298">Nodes.cu:298</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_sub_node_html_ab5c38bfc256e7784a22bb2bdcab7a72d"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_sub_node.html#ab5c38bfc256e7784a22bb2bdcab7a72d">nz::nodes::calc::SubNode::SubNode</a></div><div class="ttdeci">SubNode(Node *input_left, Node *input_right)</div><div class="ttdoc">Constructor to initialize a SubNode for tensor subtraction.</div><div class="ttdef"><b>Definition</b> <a href="#l00283">Nodes.cu:283</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_sub_node_html_abb396a092ac9fb09c3a656329132842d"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_sub_node.html#abb396a092ac9fb09c3a656329132842d">nz::nodes::calc::SubNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Backward pass for the SubNode to propagate gradients.</div><div class="ttdef"><b>Definition</b> <a href="#l00302">Nodes.cu:302</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_swish_node_html_a20f471bd6a03cf6d72d4e37eaba9fbb7"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_swish_node.html#a20f471bd6a03cf6d72d4e37eaba9fbb7">nz::nodes::calc::SwishNode::SwishNode</a></div><div class="ttdeci">SwishNode(Node *input)</div><div class="ttdoc">Constructor to initialize a SwishNode for applying the Swish activation function.</div><div class="ttdef"><b>Definition</b> <a href="#l00431">Nodes.cu:431</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_swish_node_html_a55758c143f9a941d24abc58a43ae5e9d"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_swish_node.html#a55758c143f9a941d24abc58a43ae5e9d">nz::nodes::calc::SwishNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Backward pass for the SwishNode to compute gradients.</div><div class="ttdef"><b>Definition</b> <a href="#l00444">Nodes.cu:444</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_swish_node_html_ab0fbf5a4d05c0df96b8aaffab36d92db"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_swish_node.html#ab0fbf5a4d05c0df96b8aaffab36d92db">nz::nodes::calc::SwishNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Forward pass for the SwishNode to apply the Swish activation function.</div><div class="ttdef"><b>Definition</b> <a href="#l00438">Nodes.cu:438</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_tanh_node_html_a451ff464932275955fbec1c33abdba97"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_tanh_node.html#a451ff464932275955fbec1c33abdba97">nz::nodes::calc::TanhNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Forward pass for the TanhNode to apply the tanh activation function.</div><div class="ttdef"><b>Definition</b> <a href="#l00394">Nodes.cu:394</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_tanh_node_html_ac75b18193ae5de920c0060ad83d1542a"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_tanh_node.html#ac75b18193ae5de920c0060ad83d1542a">nz::nodes::calc::TanhNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Backward pass for the TanhNode to compute gradients.</div><div class="ttdef"><b>Definition</b> <a href="#l00400">Nodes.cu:400</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1calc_1_1_tanh_node_html_aea688a8ba028a288d331ed04d8fd4871"><div class="ttname"><a href="classnz_1_1nodes_1_1calc_1_1_tanh_node.html#aea688a8ba028a288d331ed04d8fd4871">nz::nodes::calc::TanhNode::TanhNode</a></div><div class="ttdeci">TanhNode(Node *input)</div><div class="ttdoc">Constructor to initialize a TanhNode for applying the tanh activation function.</div><div class="ttdef"><b>Definition</b> <a href="#l00387">Nodes.cu:387</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1io_1_1_input_node_html_a217dbf39ca3882f5e514357f72f29458"><div class="ttname"><a href="classnz_1_1nodes_1_1io_1_1_input_node.html#a217dbf39ca3882f5e514357f72f29458">nz::nodes::io::InputNode::InputNode</a></div><div class="ttdeci">InputNode(const Tensor::shape_type &amp;shape, bool requires_grad=false)</div><div class="ttdoc">Constructor to initialize an InputNode with a specified shape and gradient requirement.</div><div class="ttdef"><b>Definition</b> <a href="#l00024">Nodes.cu:24</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1io_1_1_input_node_html_a3cde8af9401a117601dcdb0c9063516a"><div class="ttname"><a href="classnz_1_1nodes_1_1io_1_1_input_node.html#a3cde8af9401a117601dcdb0c9063516a">nz::nodes::io::InputNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Backward pass for the InputNode.</div><div class="ttdef"><b>Definition</b> <a href="#l00049">Nodes.cu:49</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1io_1_1_input_node_html_a4ba34603676c094723409d9e6b770976"><div class="ttname"><a href="classnz_1_1nodes_1_1io_1_1_input_node.html#a4ba34603676c094723409d9e6b770976">nz::nodes::io::InputNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Forward pass for the InputNode.</div><div class="ttdef"><b>Definition</b> <a href="#l00046">Nodes.cu:46</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1io_1_1_output_node_html_a1c05ec6cdbddef105a20c400d0515471"><div class="ttname"><a href="classnz_1_1nodes_1_1io_1_1_output_node.html#a1c05ec6cdbddef105a20c400d0515471">nz::nodes::io::OutputNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Forward pass for the OutputNode.</div><div class="ttdef"><b>Definition</b> <a href="#l00058">Nodes.cu:58</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1io_1_1_output_node_html_a2f76355b646a9c9f1a0972ad87f6a260"><div class="ttname"><a href="classnz_1_1nodes_1_1io_1_1_output_node.html#a2f76355b646a9c9f1a0972ad87f6a260">nz::nodes::io::OutputNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Backward pass for the OutputNode.</div><div class="ttdef"><b>Definition</b> <a href="#l00062">Nodes.cu:62</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1io_1_1_output_node_html_a7ac1292b280afcd86b31853b1275c1c4"><div class="ttname"><a href="classnz_1_1nodes_1_1io_1_1_output_node.html#a7ac1292b280afcd86b31853b1275c1c4">nz::nodes::io::OutputNode::getLoss</a></div><div class="ttdeci">Tensor::value_type getLoss() const</div><div class="ttdoc">Retrieves the loss value stored in the OutputNode.</div><div class="ttdef"><b>Definition</b> <a href="#l00068">Nodes.cu:68</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1io_1_1_output_node_html_a98af165dc12d16d812708c3cdc9097b2"><div class="ttname"><a href="classnz_1_1nodes_1_1io_1_1_output_node.html#a98af165dc12d16d812708c3cdc9097b2">nz::nodes::io::OutputNode::OutputNode</a></div><div class="ttdeci">OutputNode(Node *input)</div><div class="ttdoc">Constructor to initialize an OutputNode with a given input node.</div><div class="ttdef"><b>Definition</b> <a href="#l00052">Nodes.cu:52</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1io_1_1_output_node_html_ac340bd5a932808333e08e8bf24d53039"><div class="ttname"><a href="classnz_1_1nodes_1_1io_1_1_output_node.html#ac340bd5a932808333e08e8bf24d53039">nz::nodes::io::OutputNode::print</a></div><div class="ttdeci">void print(std::ostream &amp;os) const override</div><div class="ttdoc">Prints the type, data, gradient, and loss of the node.</div><div class="ttdef"><b>Definition</b> <a href="#l00072">Nodes.cu:72</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1loss_1_1_binary_cross_entropy_node_html_a79a7fb04d377c806b1a05306ba3bb601"><div class="ttname"><a href="classnz_1_1nodes_1_1loss_1_1_binary_cross_entropy_node.html#a79a7fb04d377c806b1a05306ba3bb601">nz::nodes::loss::BinaryCrossEntropyNode::BinaryCrossEntropyNode</a></div><div class="ttdeci">BinaryCrossEntropyNode(Node *input1, Node *input2)</div><div class="ttdoc">Constructor to initialize a BinaryCrossEntropyNode for computing the Binary Cross-Entropy loss.</div><div class="ttdef"><b>Definition</b> <a href="#l00813">Nodes.cu:813</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1loss_1_1_binary_cross_entropy_node_html_a868b2f5886b2e2adee12439ad50ca91a"><div class="ttname"><a href="classnz_1_1nodes_1_1loss_1_1_binary_cross_entropy_node.html#a868b2f5886b2e2adee12439ad50ca91a">nz::nodes::loss::BinaryCrossEntropyNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Computes the gradients of the Binary Cross-Entropy (BCE) loss with respect to the inputs during the b...</div><div class="ttdef"><b>Definition</b> <a href="#l00842">Nodes.cu:842</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1loss_1_1_binary_cross_entropy_node_html_afccd1a1a1207379dbfb648d0cbc3aab4"><div class="ttname"><a href="classnz_1_1nodes_1_1loss_1_1_binary_cross_entropy_node.html#afccd1a1a1207379dbfb648d0cbc3aab4">nz::nodes::loss::BinaryCrossEntropyNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Computes the Binary Cross-Entropy (BCE) loss in the forward pass.</div><div class="ttdef"><b>Definition</b> <a href="#l00822">Nodes.cu:822</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1loss_1_1_mean_squared_error_node_html_a8ccbbad9b8bb2111d24af789020337ce"><div class="ttname"><a href="classnz_1_1nodes_1_1loss_1_1_mean_squared_error_node.html#a8ccbbad9b8bb2111d24af789020337ce">nz::nodes::loss::MeanSquaredErrorNode::backward</a></div><div class="ttdeci">void backward() override</div><div class="ttdoc">Computes the backward pass of the Mean Squared Error (MSE) loss function.</div><div class="ttdef"><b>Definition</b> <a href="#l00804">Nodes.cu:804</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1loss_1_1_mean_squared_error_node_html_ac9e098d63556329314c7389b779169b6"><div class="ttname"><a href="classnz_1_1nodes_1_1loss_1_1_mean_squared_error_node.html#ac9e098d63556329314c7389b779169b6">nz::nodes::loss::MeanSquaredErrorNode::MeanSquaredErrorNode</a></div><div class="ttdeci">MeanSquaredErrorNode(Node *input1, Node *input2)</div><div class="ttdoc">Constructor to initialize a MeanSquaredErrorNode for computing the Mean Squared Error (MSE) loss.</div><div class="ttdef"><b>Definition</b> <a href="#l00776">Nodes.cu:776</a></div></div>
<div class="ttc" id="aclassnz_1_1nodes_1_1loss_1_1_mean_squared_error_node_html_ae81d6afb059f76617ea034032c12ec13"><div class="ttname"><a href="classnz_1_1nodes_1_1loss_1_1_mean_squared_error_node.html#ae81d6afb059f76617ea034032c12ec13">nz::nodes::loss::MeanSquaredErrorNode::forward</a></div><div class="ttdeci">void forward() override</div><div class="ttdoc">Computes the forward pass of the Mean Squared Error (MSE) loss function.</div><div class="ttdef"><b>Definition</b> <a href="#l00785">Nodes.cu:785</a></div></div>
<div class="ttc" id="anamespacenz_1_1data_html_a7503b6894e8052ed54eb169550d135c0"><div class="ttname"><a href="namespacenz_1_1data.html#a7503b6894e8052ed54eb169550d135c0">nz::data::tensorMatrixSub</a></div><div class="ttdeci">std::enable_if_t&lt; is_valid_tensor_type&lt; T &gt;::value, void &gt; tensorMatrixSub(T &amp;out, const T &amp;lhs, const T &amp;rhs)</div><div class="ttdoc">Performs matrix subtraction operation on tensors with broadcast compatibility.</div><div class="ttdef"><b>Definition</b> <a href="_tensor_operations_8cuh_source.html#l00858">TensorOperations.cuh:858</a></div></div>
<div class="ttc" id="anamespacenz_1_1data_html_a8cf4ac2437dd67698684169bebb225d4"><div class="ttname"><a href="namespacenz_1_1data.html#a8cf4ac2437dd67698684169bebb225d4">nz::data::tensorMatrixAdd</a></div><div class="ttdeci">std::enable_if_t&lt; is_valid_tensor_type&lt; T &gt;::value, void &gt; tensorMatrixAdd(T &amp;out, const T &amp;lhs, const T &amp;rhs)</div><div class="ttdoc">Performs matrix addition operation on tensors with broadcast compatibility.</div><div class="ttdef"><b>Definition</b> <a href="_tensor_operations_8cuh_source.html#l00787">TensorOperations.cuh:787</a></div></div>
<div class="ttc" id="anamespacenz_1_1data_html_ac8d64dd271e9a2e50682e733bd14ec19"><div class="ttname"><a href="namespacenz_1_1data.html#ac8d64dd271e9a2e50682e733bd14ec19">nz::data::transpose</a></div><div class="ttdeci">std::enable_if_t&lt; is_valid_tensor_type&lt; T &gt;::value, T &gt; transpose(const T &amp;in)</div><div class="ttdoc">Transposes a tensor with a valid tensor type.</div><div class="ttdef"><b>Definition</b> <a href="_tensor_operations_8cuh_source.html#l01073">TensorOperations.cuh:1073</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a04246c5218530f789a0ed4811b7ef3f3"><div class="ttname"><a href="namespacenz_1_1krnl.html#a04246c5218530f789a0ed4811b7ef3f3">nz::krnl::LeakyReLU</a></div><div class="ttdeci">void LeakyReLU(dim3 gridDim, dim3 blockDim, float *out, float *in, unsigned long long n, float alpha=0.01f)</div><div class="ttdoc">Kernel function to apply the Leaky ReLU activation function on the GPU.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00315">OperationKernels.cu:315</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a0e82aca250b46ac8ded8cae8936d7e38"><div class="ttname"><a href="namespacenz_1_1krnl.html#a0e82aca250b46ac8ded8cae8936d7e38">nz::krnl::ExponentialLinearUnit</a></div><div class="ttdeci">void ExponentialLinearUnit(dim3 gridDim, dim3 blockDim, float *out, float *in, unsigned long long n, float alpha=1.0f)</div><div class="ttdoc">Kernel function to apply the Exponential Linear Unit (ELU) activation function on the GPU.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00372">OperationKernels.cu:372</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a0ed44a68bfb86a9fd3d6c3b25614713f"><div class="ttname"><a href="namespacenz_1_1krnl.html#a0ed44a68bfb86a9fd3d6c3b25614713f">nz::krnl::gradCopy</a></div><div class="ttdeci">void gradCopy(dim3 gridDim, dim3 blockDim, float *out, float *in, size_t n, const std::vector&lt; size_t &gt; &amp;offset_o, const std::vector&lt; size_t &gt; &amp;offset_i)</div><div class="ttdoc">Copies gradient data from one array to another with specified offsets.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l01238">OperationKernels.cu:1238</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a1fc3d553947a5cad87f29989f9d9465d"><div class="ttname"><a href="namespacenz_1_1krnl.html#a1fc3d553947a5cad87f29989f9d9465d">nz::krnl::BCEBackward</a></div><div class="ttdeci">void BCEBackward(dim3 gridDim, dim3 blockDim, float *out, float *predict, float *real, unsigned long long n)</div><div class="ttdoc">Kernel function to compute the gradient of Binary Cross Entropy (BCE) loss for backpropagation.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00701">OperationKernels.cu:701</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a21bbbcf6d97bfaccc828ce7736814bd4"><div class="ttname"><a href="namespacenz_1_1krnl.html#a21bbbcf6d97bfaccc828ce7736814bd4">nz::krnl::Sigmoid</a></div><div class="ttdeci">void Sigmoid(dim3 gridDim, dim3 blockDim, float *out, float *in, unsigned long long n)</div><div class="ttdoc">Kernel function to apply the Sigmoid activation function on the GPU.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00263">OperationKernels.cu:263</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a27bc4025be4253d5fffae2bf1b43b3af"><div class="ttname"><a href="namespacenz_1_1krnl.html#a27bc4025be4253d5fffae2bf1b43b3af">nz::krnl::ScalarDiv</a></div><div class="ttdeci">void ScalarDiv(dim3 gridDim, dim3 blockDim, float *out, float *in, float num, unsigned long long n)</div><div class="ttdoc">Kernel function to perform scalar division on the GPU.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00183">OperationKernels.cu:183</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a43232f9472ad3b974351e59386208efa"><div class="ttname"><a href="namespacenz_1_1krnl.html#a43232f9472ad3b974351e59386208efa">nz::krnl::HardSigmoidBackward</a></div><div class="ttdeci">void HardSigmoidBackward(dim3 gridDim, dim3 blockDim, float *A_grad, float *A, float *B_grad, unsigned long long n, float alpha=0.2f, float beta=0.5f)</div><div class="ttdoc">Kernel function to compute the gradient of the Hard Sigmoid activation during backpropagation.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00424">OperationKernels.cu:424</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a454a28ef0e22014efca1ede4e954db65"><div class="ttname"><a href="namespacenz_1_1krnl.html#a454a28ef0e22014efca1ede4e954db65">nz::krnl::Compress</a></div><div class="ttdeci">void Compress(dim3 gridDim, dim3 blockDim, float *out, float *in, size_t n, size_t total)</div><div class="ttdoc">Compresses the input array into the output array with a specified total size.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l01303">OperationKernels.cu:1303</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a455365870d43ff26687a731d15c4cdff"><div class="ttname"><a href="namespacenz_1_1krnl.html#a455365870d43ff26687a731d15c4cdff">nz::krnl::HardSwishBackward</a></div><div class="ttdeci">void HardSwishBackward(dim3 gridDim, dim3 blockDim, float *A_grad, float *A, float *B_grad, unsigned long long n, float alpha=0.2f, float beta=0.5f)</div><div class="ttdoc">Kernel function to compute the gradient of the Hard Swish activation during backpropagation.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00462">OperationKernels.cu:462</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a4ddfc808de99fe831e74a3bd3f9bbdaf"><div class="ttname"><a href="namespacenz_1_1krnl.html#a4ddfc808de99fe831e74a3bd3f9bbdaf">nz::krnl::ReLUBackward</a></div><div class="ttdeci">void ReLUBackward(dim3 gridDim, dim3 blockDim, float *A_grad, float *A, float *B_grad, unsigned long long n)</div><div class="ttdoc">Kernel function to compute the gradient of the ReLU activation during backpropagation.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00250">OperationKernels.cu:250</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a52e449285e560185378234aecaf2f87c"><div class="ttname"><a href="namespacenz_1_1krnl.html#a52e449285e560185378234aecaf2f87c">nz::krnl::HardSigmoid</a></div><div class="ttdeci">void HardSigmoid(dim3 gridDim, dim3 blockDim, float *out, float *in, unsigned long long n, float alpha=0.2f, float beta=0.5f)</div><div class="ttdoc">Kernel function to apply the Hard Sigmoid activation function on the GPU.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00403">OperationKernels.cu:403</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a56f84e531825be8b2b0974c2488eb765"><div class="ttname"><a href="namespacenz_1_1krnl.html#a56f84e531825be8b2b0974c2488eb765">nz::krnl::ScalarAdd</a></div><div class="ttdeci">void ScalarAdd(dim3 gridDim, dim3 blockDim, float *out, float *in, float num, unsigned long long n)</div><div class="ttdoc">Kernel function to add a scalar to each element of a matrix on the GPU.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00196">OperationKernels.cu:196</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a5af716524e248c61f3dce227d8ef6e34"><div class="ttname"><a href="namespacenz_1_1krnl.html#a5af716524e248c61f3dce227d8ef6e34">nz::krnl::ScalarMul</a></div><div class="ttdeci">void ScalarMul(dim3 gridDim, dim3 blockDim, float *out, float *in, float num, unsigned long long n)</div><div class="ttdoc">Kernel function to perform scalar multiplication on the GPU.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00170">OperationKernels.cu:170</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a6c5a4b54442aab42df5afe8688e71596"><div class="ttname"><a href="namespacenz_1_1krnl.html#a6c5a4b54442aab42df5afe8688e71596">nz::krnl::SwishBackward</a></div><div class="ttdeci">void SwishBackward(dim3 gridDim, dim3 blockDim, float *A_grad, float *A, float *B, float *B_grad, unsigned long long n)</div><div class="ttdoc">Kernel function to compute the gradient of the Swish activation during backpropagation.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00359">OperationKernels.cu:359</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a7eade95ddcf48141d69bb19803b22d51"><div class="ttname"><a href="namespacenz_1_1krnl.html#a7eade95ddcf48141d69bb19803b22d51">nz::krnl::LeakyReLUBackward</a></div><div class="ttdeci">void LeakyReLUBackward(dim3 gridDim, dim3 blockDim, float *A_grad, float *A, float *B_grad, unsigned long long n, float alpha=0.01f)</div><div class="ttdoc">Kernel function to compute the gradient of the Leaky ReLU activation during backpropagation.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00330">OperationKernels.cu:330</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a8855f411733f7de29d013f4ad40096c9"><div class="ttname"><a href="namespacenz_1_1krnl.html#a8855f411733f7de29d013f4ad40096c9">nz::krnl::RectifiedLinearUnit</a></div><div class="ttdeci">void RectifiedLinearUnit(dim3 gridDim, dim3 blockDim, float *out, float *in, unsigned long long n)</div><div class="ttdoc">Kernel function to apply the Rectified Linear Unit (ReLU) activation on the GPU.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00237">OperationKernels.cu:237</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a90d501e72361b7341f36394af0f27c74"><div class="ttname"><a href="namespacenz_1_1krnl.html#a90d501e72361b7341f36394af0f27c74">nz::krnl::TanhBackward</a></div><div class="ttdeci">void TanhBackward(dim3 gridDim, dim3 blockDim, float *A_grad, float *B, float *B_grad, unsigned long long n)</div><div class="ttdoc">Kernel function to compute the gradient of the Tanh activation during backpropagation.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00302">OperationKernels.cu:302</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a997aa5460fd64fadf9b701fbf73e3fb2"><div class="ttname"><a href="namespacenz_1_1krnl.html#a997aa5460fd64fadf9b701fbf73e3fb2">nz::krnl::Swish</a></div><div class="ttdeci">void Swish(dim3 gridDim, dim3 blockDim, float *out, float *in, unsigned long long n)</div><div class="ttdoc">Kernel function to apply the Swish activation function on the GPU.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00344">OperationKernels.cu:344</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_a9ac0590fbb5eb7f51b05da574e9845a8"><div class="ttname"><a href="namespacenz_1_1krnl.html#a9ac0590fbb5eb7f51b05da574e9845a8">nz::krnl::NgradCopy</a></div><div class="ttdeci">void NgradCopy(dim3 gridDim, dim3 blockDim, float *out, float *in, size_t n, const std::vector&lt; size_t &gt; &amp;offset_o, const std::vector&lt; size_t &gt; &amp;offset_i)</div><div class="ttdoc">Copies gradient data from one array to another with specified offsets.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l01264">OperationKernels.cu:1264</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_abf927faf0950fbc215564c67b8ac57be"><div class="ttname"><a href="namespacenz_1_1krnl.html#abf927faf0950fbc215564c67b8ac57be">nz::krnl::BinaryCrossEntropy</a></div><div class="ttdeci">void BinaryCrossEntropy(dim3 gridDim, dim3 blockDim, size_t sharedMemSize, float *out, float *predict, float *real, unsigned long long n)</div><div class="ttdoc">Kernel function to compute the Binary Cross Entropy (BCE) loss between predicted and real values.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00686">OperationKernels.cu:686</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_adbafc409d57fa0a9d78ecac5bf7b10a3"><div class="ttname"><a href="namespacenz_1_1krnl.html#adbafc409d57fa0a9d78ecac5bf7b10a3">nz::krnl::Softmax</a></div><div class="ttdeci">void Softmax(dim3 gridDim, dim3 blockDim, float *out, float *in, float exp_sum_of_input, unsigned long long n, size_t offset=0)</div><div class="ttdoc">Kernel function to apply the Softmax function on the GPU.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00525">OperationKernels.cu:525</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_ae45dbebceb76ddf82fa5e6b9df882e62"><div class="ttname"><a href="namespacenz_1_1krnl.html#ae45dbebceb76ddf82fa5e6b9df882e62">nz::krnl::Expand</a></div><div class="ttdeci">void Expand(dim3 gridDim, dim3 blockDim, float *out, float *in, size_t n, size_t total)</div><div class="ttdoc">Expands the input array into the output array with a specified total size.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l01290">OperationKernels.cu:1290</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_ae77920db6adf79a17dbfb1dbf1ab5656"><div class="ttname"><a href="namespacenz_1_1krnl.html#ae77920db6adf79a17dbfb1dbf1ab5656">nz::krnl::MSEBackward</a></div><div class="ttdeci">void MSEBackward(dim3 gridDim, dim3 blockDim, float *out, float *predict, float *real, unsigned long long n)</div><div class="ttdoc">Kernel function to compute the gradient of the Mean Squared Error (MSE) loss for backpropagation.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00629">OperationKernels.cu:629</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_aeb7d10939b25508e0b5db1fe44f4b467"><div class="ttname"><a href="namespacenz_1_1krnl.html#aeb7d10939b25508e0b5db1fe44f4b467">nz::krnl::Tanh</a></div><div class="ttdeci">void Tanh(dim3 gridDim, dim3 blockDim, float *out, float *in, unsigned long long n)</div><div class="ttdoc">Kernel function to apply the Tanh activation function on the GPU.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00289">OperationKernels.cu:289</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_aee8ca471aa260bd1fca5b1797e229f9f"><div class="ttname"><a href="namespacenz_1_1krnl.html#aee8ca471aa260bd1fca5b1797e229f9f">nz::krnl::ELUBackward</a></div><div class="ttdeci">void ELUBackward(dim3 gridDim, dim3 blockDim, float *A_grad, float *A, float *B_grad, unsigned long long n, float alpha=1.0f)</div><div class="ttdoc">Kernel function to compute the gradient of the ELU activation during backpropagation.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00388">OperationKernels.cu:388</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_aef9c028ed356b5684e103639bb23bcf0"><div class="ttname"><a href="namespacenz_1_1krnl.html#aef9c028ed356b5684e103639bb23bcf0">nz::krnl::HardSwish</a></div><div class="ttdeci">void HardSwish(dim3 gridDim, dim3 blockDim, float *out, float *in, unsigned long long n, float alpha=0.2f, float beta=0.5f)</div><div class="ttdoc">Kernel function to apply the Hard Swish activation function on the GPU.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00445">OperationKernels.cu:445</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_af76ce6a930db4def5ceb51350af72f3c"><div class="ttname"><a href="namespacenz_1_1krnl.html#af76ce6a930db4def5ceb51350af72f3c">nz::krnl::MeanSquaredError</a></div><div class="ttdeci">void MeanSquaredError(dim3 gridDim, dim3 blockDim, size_t sharedMemSize, float *out, float *predict, float *real, unsigned long long n)</div><div class="ttdoc">Kernel function to compute the Mean Squared Error (MSE) loss between predicted and real values.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00615">OperationKernels.cu:615</a></div></div>
<div class="ttc" id="anamespacenz_1_1krnl_html_aff1f9f1bf9fb677024bd2b565fab9801"><div class="ttname"><a href="namespacenz_1_1krnl.html#aff1f9f1bf9fb677024bd2b565fab9801">nz::krnl::SigmoidBackward</a></div><div class="ttdeci">void SigmoidBackward(dim3 gridDim, dim3 blockDim, float *A_grad, float *B, float *B_grad, unsigned long long n)</div><div class="ttdoc">Kernel function to compute the gradient of the Sigmoid activation during backpropagation.</div><div class="ttdef"><b>Definition</b> <a href="_operation_kernels_8cu_source.html#l00277">OperationKernels.cu:277</a></div></div>
<div class="ttc" id="anamespacenz_1_1nodes_html"><div class="ttname"><a href="namespacenz_1_1nodes.html">nz::nodes</a></div><div class="ttdoc">Contains classes and functionality for nodes in a neural network or computational graph.</div></div>
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